Rapid determination of microbial drowth and antimicrobial suseptability

ABSTRACT

This disclosure is related to systems and methods for rapid determination of microorganism growth and antimicrobial agent susceptibility and/or resistance.

CROSS REFERENCE TO RELATED U.S. APPLICATIONS

This Patent Cooperation Treaty application claims priority to U.S.Patent Application No. 61/798,105 entitled “Rapid Determination ofMicrobial Growth and Antimicrobial Susceptibility” and filed Mar. 15,2013, the contents of which are hereby incorporated by reference intheir entirety.

FIELD

Systems, methods, computer programs, and computer readable mediums forevaluating microorganisms are described.

BACKGROUND

Critically ill patients that acquire a microbial infection must begineffective antimicrobial therapy as quickly as possible. Application ofthe computer-based systems, devices, methods, and computer programs ofthe present disclosure to provide organism detection, determination ofgrowth, identification, antimicrobial susceptibility testing, and/orantimicrobial resistance characterization may substantially reduce thetime from sample to result, thereby increasing the potential forsuccessful therapeutic outcomes.

SUMMARY

In various aspects, systems, methods and computer readable mediums areprovided evaluating microorganisms. In a first aspect, a computer-basedsystem configured to analyze microorganism information is providedhaving one or more processors and a tangible, non-transitory memory. Thecomputer-based system may also have a user interface. A first valueassociated with an attribute of a microorganism is determined by thecomputer-based system, based on first information obtained from amicroorganism detection system. A second value associated with anattribute of the microorganism is determined, optionally by thecomputer-based system, based on second information obtained from amicroorganism detection system. A growth rate is determined by referenceto the first value and the second value. The determined growth rate iscompared to a control growth rate.

In a second aspect, a computer-based system configured to analyzemicroorganism information is provided having one or more processors anda tangible, non-transitory memory. The computer-based system may alsohave a user interface. A first value associated with an attribute of amicroorganism is determined by the computer-based system, based on firstinformation obtained from a microorganism detection system. A secondvalue associated with an attribute of the microorganism is determined bythe computer-based system based on second information obtained from amicroorganism detection system. A growth rate is determined, optionallyby the computer-based system, by reference to the first value and thesecond value. The determined growth rate is compared to a control growthrate.

In a third aspect, a computer-based system configured to analyzemicroorganism information is provided having one or more processors anda tangible, non-transitory memory. The computer-based system may alsohave a user interface. A first value associated with an attribute of amicroorganism is determined by the computer-based system, based on firstinformation obtained from a microorganism detection system. A secondvalue associated with an attribute of the microorganism is determined bythe computer-based system based on second information obtained from amicroorganism detection system. A growth rate is determined by thecomputer-based system by reference to the first value and the secondvalue. The determined growth rate is compared, optionally by thecomputer-based system, to a control growth rate.

In another aspect, systems, methods and computer readable mediumscapable of determining a growth rate of one or more microorganisms aredescribed. A computer-based system configured to analyze microorganisminformation is provided having one or more processors and a tangible,non-transitory memory. The computer-based system may also have a userinterface. A first value associated with a growth rate of amicroorganism is determined by the computer-based system, based oninformation obtained from a microorganism detection system, optionallyin response to subjecting a microorganism to at least one of a firstevent and a first condition. A second value associated with a growthrate of a reference microorganism is obtained, optionally from amicroorganism detection system. A proportional relationship between thefirst value and the second value may be compared by the computer-basedsystem.

In various embodiments, the systems, methods and computer readablemediums described may be capable of evaluating microorganisms. Forexample, a method may comprise: determining, by a computer-based systemconfigured to analyze microorganism information and comprising aprocessor and a tangible, non-transitory memory, a first valueassociated with an attribute of a microorganism, based on firstinformation from a microorganism detection system; determining, by thecomputer-based system, a second value associated with the attribute ofthe microorganism, based on second information from the microorganismdetection system; determining, by the computer-based system, a growthrate associated with the first value and the second value; andcomparing, by the computer-based system, the growth rate to a controlgrowth rate.

In various embodiments, the systems, methods and computer readablemediums described may be capable of determining a growth rate of one ormore microorganisms. For example, the method may comprise: determining,by a computer-based system configured to analyze microorganisminformation and comprising a processor and a tangible, non-transitorymemory, a first value corresponding to a growth rate of a microorganism,based on information from a microorganism detection system, in responseto subjecting a microorganism to at least one of a first event and afirst condition; obtaining, by the computer-based system, a second valuecorresponding to a growth rate of a reference microorganism; anddetermining, by the computer-based system, a proportional relationshipbetween the first value to second value.

In various embodiments, the systems, methods and computer readablemediums described may be capable of determining a growth rate of one ormore microorganisms. A method may comprise: detecting, by acomputer-based system configured to analyze microorganism informationand comprising a processor and a tangible, non-transitory memory, firstmicroorganism information from a microorganism detection system;detecting, by the computer-based system, second microorganisminformation from the microorganism detection system; parsing, by thecomputer-based system, first microorganism information and secondmicroorganism information into a plurality of microorganism informationvalue subsets, wherein a first microorganism information value subsetcreated from first microorganism information and second microorganisminformation value subset created from second microorganism informationare associated with a location; associating, by the computer-basedsystem, the first microorganism information value subset and the secondmicroorganism information value subset; determining, by thecomputer-based system, a first growth rate of a microorganism, based onthe first microorganism information value subset and the secondmicroorganism information value subset, in response to subjecting amicroorganism to at least one of a first event and a first condition;obtaining, by the computer-based system, a second value corresponding toa reference growth rate; and determining, by the computer-based system,a proportional relationship between the first value to second value.

In various embodiments, the second value may be determined in responseto an event.

In various embodiments, the second value may be determined in responseto a second condition and a second event.

In various embodiments, the microorganism may be an individuatedmicroorganism.

In various embodiments, the microorganism may be subjected to acondition.

In various embodiments, the condition may be associated with the event.

In various embodiments, the control growth rate may be at least one of apredetermined growth rate and a dynamically determined growth rate.

In various embodiments, the event may be at least one of a predeterminedtime, a dynamically determined mass, a number of individuatedmicroorganisms, and a number of clones.

In various embodiments, the condition may be at least one of atemperature, a growth medium condition, a carbon source, a nitrogensource, an amino acid, a nutrient, a salt, a metal ion, a cofactor, apH, a trace element, a dissolved gas, an antimicrobial agent, an aerobiccondition, and an anaerobic condition.

In various embodiments, the condition may be at least one of static anddynamic. In various embodiments, the microorganism information maycomprise a plurality of values associated with a plurality of attributesevaluated simultaneously.

In various embodiments, a change in measured signal intensity associatedwith a microorganism attribute or clone, a clone signal intensity curveshape likelihood, or another variant response function is determined bya computer-based system.

In various embodiments, a tracking error likelihood is determined by acomputer-based system.

In various embodiments, a growth likelihood value is determined, by acomputer-based system, based on the clone signal intensity curve shapelikelihood and tracking error likelihood.

In various embodiments, microorganism susceptibility is determined, by acomputer-based system, based on a comparison of the growth likelihoodvalue to a reference range.

In various embodiments, a signal associated with the microorganism isrendered, by a computer-based system, into a plurality of signalapproximations.

In various embodiments, the plurality of signal approximations areplanes comprising a plurality of point amplitudes corresponding tomicroorganism locations.

In various embodiments, the plurality of signal approximations arecombined, by a computer-based system, to create a microorganism model.

In various embodiments, the point amplitudes are analyzed, by acomputer-based system, in association with at least one of backgroundinformation and noise information.

In various embodiments, the plurality of signal approximations arefiltered, by a computer-based system, to eliminate at least one ofbackground information and noise information.

In various embodiments, locations associated with point amplitudescorresponding to microorganisms are registered by a computer-basedsystem.

In various embodiments, a second value may be obtained from a referencegrowth curve associated with a reference microorganism.

In various embodiments, an event may include at least one of apredetermined time, a dynamically determined mass, a number ofindividuated microorganisms, and a number of clones.

In various embodiments, a condition may be at least one of atemperature, a growth medium condition, a carbon source, a nitrogensource, an amino acid, a nutrient, a salt, a metal ion, a cofactor, apH, a trace element, a dissolved gas, an antimicrobial agent, an aerobiccondition, and an anaerobic condition.

In various embodiments, the proportional relationship between a firstgrowth rate value and a second growth rate value may be evaluated,optionally by a computer-based system, against a reference range.

In various embodiments, at least one of the following is identified, bya computer-based system: microorganism susceptibility to anantimicrobial agent, microorganism resistance to an antimicrobial agent,microorganism expression of a virulence factor, microorganismhypervirulence, and polymicrobial specimens.

In various embodiments, at least one of the following is identified, bya computer-based system: microorganism susceptibility to anantimicrobial agent, microorganism resistance to an antimicrobial agent,microorganism expression of a virulence factor, microorganismhypervirulence, and polymicrobial specimens.

In various embodiments, at least one of the following is identified, inassociation with a computer-based system: microorganism susceptibilityto an antimicrobial agent, microorganism resistance to an antimicrobialagent, microorganism expression of a virulence factor, microorganismhypervirulence, and polymicrobial specimens.

In various embodiments, microorganism susceptibility to an antimicrobialagent is identified, by a computer-based system, in response to theproportional relationship between the first growth rate value and thesecond growth rate value falling one of within and outside of thereference range.

In various embodiments, microorganism susceptibility to an antimicrobialagent is identified, in association with a computer-based system, inresponse to the proportional relationship between the first growth ratevalue and the second growth rate value falling one of within and outsideof the reference range.

In various embodiments, a microorganism that is not susceptible to anantimicrobial agent is identified, by a computer-based system, inresponse to the proportional relationship falling within the referencerange.

In various embodiments, a microorganism that is not susceptible to anantimicrobial agent is identified, in association with a computer-basedsystem, in response to the proportional relationship falling within thereference range.

In various embodiments, a microorganism that is not susceptible to anantimicrobial agent is identified, by a computer-based system, inresponse to the proportional relationship falling outside of thereference range.

In various embodiments, a microorganism that is not susceptible to anantimicrobial agent is identified, in association with a computer-basedsystem, in response to the proportional relationship falling outside ofthe reference range.

In various embodiments, a microorganism that is resistant to anantimicrobial agent is identified, by a computer-based system, inresponse to the proportional relationship falling within the referencerange.

In various embodiments, a microorganism that is resistant to anantimicrobial agent is identified, in association with a computer-basedsystem, in response to the proportional relationship falling within thereference range.

In various embodiments, a microorganism that is resistant to anantimicrobial agent is identified, by a computer-based system, inresponse to the proportional relationship falling outside of thereference range.

In various embodiments, a microorganism that is resistant to anantimicrobial agent is identified, in association with a computer-basedsystem, in response to the proportional relationship falling outside ofthe reference range.

In various embodiments, a microorganism expressing a virulence factor isidentified, by a computer-based system, in response to the proportionalrelationship falling within the reference range.

In various embodiments, a microorganism expressing a virulence factor isidentified, in association with a computer-based system, in response tothe proportional relationship falling within the reference range.

In various embodiments, a microorganism expressing a virulence factor isidentified, by a computer-based system, in response to the proportionalrelationship being outside of the reference range.

In various embodiments, a microorganism expressing a virulence factor isidentified, in association with a computer-based system, in response tothe proportional relationship being outside of the reference range.

In various embodiments, a microorganism that is hypervirulent isidentified, by a computer-based system, in response to the proportionalrelationship being within the reference range.

In various embodiments, a microorganism that is hypervirulent isidentified, in association with a computer-based system, in response tothe proportional relationship being within the reference range.

In various embodiments, a microorganism that is hypervirulent isidentified, by a computer-based system, in response to the proportionalrelationship being outside of the reference range.

In various embodiments, a microorganism that is hypervirulent isidentified, in association with a computer-based system, in response tothe proportional relationship being outside of the reference range.

In various embodiments, a polymicrobial specimen is identified, by acomputer-based system, in response to two or more proportionalrelationships falling within and/or outside of a reference range.

In various embodiments, a polymicrobial specimen is identified, inassociation with a computer-based system, in response to two or moreproportional relationships falling within and/or outside of a referencerange.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a furtherunderstanding of the disclosure and are incorporated in and constitute apart of this specification, illustrate aspects and embodiments of thedisclosure, and together with the description, serve to explain theprinciples of the disclosure, wherein:

FIG. 1 illustrates a block diagram of an exemplary system for evaluatingone or more microorganisms.

FIG. 2 illustrates an exemplary process for evaluating microorganisms.

FIG. 3 illustrates an exemplary process for evaluating microorganisms.

FIGS. 4A and 4B illustrate an exemplary cassette and flowcell channel,respectively, of a microorganism evaluation device.

FIGS. 5A-5C illustrate an exemplary electrokinetic concentration (EKC)of S. aureus.

FIGS. 6A-6D illustrate exemplary evaluation data from immunolabeling ofselected bacterial strains of Klebsiella pneumoniae ATCC 49472 andHaemophilus influenzae ATCC

FIGS. 7A-7C illustrate a series of darkfield images of Acinetobacterbaumannii showing growth over 90 minute period, with computer-basedsystem derived clonal analysis.

FIG. 8 illustrates doubling times in minutes of ten bacterial strainsmeasured with various systems described herein.

FIGS. 9A and 9B illustrate growth and kill curves of a set ofexperiments in which Klebsiella pneumoniae was concentrated, grown, anddosed with varying concentrations of imipenem.

FIGS. 10A-10C illustrate time-lapse dark-field images of individualgrowing clones (GC).

FIG. 11 illustrates growth curves of an extended-spectrum beta-lactamasepositive (ESBL+) bacterial strain and a ESBL-negative (ESBL−) bacterialstrain.

FIGS. 12A-12C illustrate K. pneumoniae ESBL+ growth in variousconditions.

FIG. 13 illustrates a receiver operating characteristic (ROC) for therules used in testing in association with the computer-based system.

FIG. 14 illustrates a scatter plot of mass ratios for two antimicrobialagents with and without combination with clavulanic acid (CA).

FIGS. 15A-15D illustrate darkfield images of Acinetobacter baumannii(AB) in 32 μg/mL sulbactam at approximately time=0 and 60 minutes. Scalebars=5 μm.

FIG. 16 illustrates a scatter plot of multiplexed automated digitalmicroscopy (MADM) system-determined Area Under the Curve (AUC) forabbreviated population analysis profiles (PAP) vs. broth microdilutionBMD-PAP-AUC (arbitrary units for areas).

FIG. 17 illustrates heteroresistant vancomycin-intermediateStaphylococcus aureus (hVISA) strain 2-9B images at approximately 0,120, and 240 minutes (horizontal labels and columns) in differentvancomycin (VAN) concentrations (μg/mL, rows).

FIGS. 18A and 18 B illustrate computer-imposed ellipses indicatingpotential organisms (pixel blobs) tracked for growth during analysis.

FIGS. 19A and 19B illustrates non-fermenter clinical isolates at the endof a 3 hour of antimicrobial agent exposure.

FIGS. 20A-20C illustrates a carbapenemase-producing K. pneumoniae (KPC)clinical isolate growth under various conditions.

FIG. 21 illustrates KPC assays under various conditions.

FIGS. 22A-22C illustrate microorganisms in a flowcell before and aftersurface

FIGS. 23A-23C illustrate live and formalin-killed A. baumannii (ATCC19606).

FIGS. 24A-24C illustrate examples of individual clones of ATCC 19606Acinetobacter.

FIGS. 25A-25C illustrate mixed species, A. baumannii ATCC 19606 and P.aeruginosa ATCC 35554.

FIGS. 26A and 26B illustrate multi-resistant S. aureus (MRSA)identification using cefoxitin (FOX) induction (negative growth ratesignifies cell lysis) and MLSB identification using erythromycin (ERY)induction and clindamycin (CLI) challenge in accordance with variousembodiments.

FIGS. 27A and 27B illustrate examples of a second-order surface and amodel mask, respectively.

FIG. 28 illustrates microorganisms in an array of wells prospectivelyused for impedance detection.

FIG. 29 illustrates prospective impedance data.

FIG. 30A-30D illustrate prospective composite graphical representationsof impedance data.

FIG. 31A-31D illustrate prospective composite images of individuatedclones.

FIG. 32 illustrates a prospective example of a graphical representationof impedance data.

DETAILED DESCRIPTION OF THE ILLUSTRATED EMBODIMENTS

The detailed description of various aspects and embodiments herein makesreference to the accompanying drawing figures, which show variousaspects and embodiments and implementations thereof by way ofillustration and best mode, and not of limitation. While these aspectsand embodiments are described in sufficient detail to enable thoseskilled in the art to practice the embodiments, it should be understoodthat other aspects and embodiments may be realized and that mechanicaland other changes may be made without departing from the spirit andscope of the present disclosure. Furthermore, any reference to singularincludes plural aspects and embodiments, and any reference to more thanone component may include a singular aspect and embodiment. Likewise,any ordination of a device, system, or method or of a component orportion thereof with designations such as “first” and “second” is forpurposes of convenience and clarity and should not be construed aslimiting or signifying more than an arbitrary distinction. Moreover,recitation of multiple aspects and embodiments having stated features isnot intended to exclude other aspects and embodiments having additionalfeatures or other aspects and embodiments incorporating differentcombinations of the stated features.

Systems, methods and computer program products are provided in variousaspects and embodiments of the present disclosure. References to“various embodiments,” “one embodiment,” “an embodiment,” “an exampleembodiment,” etc., indicate that the embodiment described may include aparticular feature, structure, or characteristic, but every embodimentmay not necessarily include the particular feature, structure, orcharacteristic. Moreover, such phrases are not necessarily referring tothe same embodiment. Further, when a particular feature, structure, orcharacteristic is described in connection with an embodiment, it issubmitted that it is within the knowledge of one skilled in the art toaffect such feature, structure, or characteristic in connection withother embodiments whether or not explicitly described. After reading thedescription, it will be apparent to one skilled in the relevant art(s)how to implement the disclosure in alternative embodiments.

Various aspects and embodiments of the present disclosure can berealized by any number of systems, computer hardware, devices, computersoftware and computer readable mediums, devices compositions, organisms,processes and methods configured to perform the intended functions.Stated differently, other systems, devices, methods, and the like can beimplemented or incorporated herein to perform the intended functions. Itshould also be noted that the accompanying drawing figures referred toherein are not drawn to scale, but can be exaggerated to illustratevarious aspects of the present disclosure, and in that regard, thedrawing figures should not be construed as limiting. Finally, althoughthe present disclosure can be described in connection with variousprinciples and beliefs, the present disclosure is not intended to bebound by any particular theory.

In various aspects, the systems, methods and computer readable medium(collectively “systems”) described herein are capable of determiningmicroorganism information (e.g., data describing a microorganismattribute). More specifically, the systems described herein are capableof identifying and quantifying individuated microorganismcharacteristics (e.g., growth). The microorganism information may beused to identify and characterize one or more microorganisms in aspecimen or sample and/or recommend treatment options based on amicroorganism event (e.g., inclusion or exclusion of one or moreantimicrobial agents from a treatment regimen).

In various aspects, the systems can identify individuated microorganismsand evaluate microorganism growth under or in response to variousconditions. For example, the microorganism may be exposed to a firstcondition that stimulates growth (e.g., an increase in temperature)and/or a second condition that inhibits growth (e.g., an antimicrobialagent). As such, the system can be capable of determining microorganismidentification, growth, antimicrobial susceptibility and/or resistance,and/or providing a variety of analytical outputs based on amulti-variable or multi-factorial analysis.

In various aspects, the systems described herein may be implemented ashardware, hardware-software, or software elements. These systems maycomprise one or more modules, analyzers, hardware components, softwarecomponents, computer programs and/or the like.

DEFINITIONS Definition of Microorganism

As used herein, the terms “microorganism” and “organism” mean a memberof one of following classes: bacteria, fungi, algae, and protozoa, andcan also include, for purposes of the present disclosure, viruses,prions, or other pathogens. In various embodiments, bacteria, and inparticular, human and animal pathogens, are evaluated. Suitablemicroorganisms include any of those well established in the medical artand those novel pathogens and variants that emerge from time to time.Examples of currently known bacterial pathogens, for example, include,but are not limited to genera such as Bacillus, Vibrio, Escherichia,Shigella, Salmonella, Mycobacterium, Clostridium, Cornyebacterium,Streptococcus, Staphylococcus, Haemophilus, Neissena, Yersinia,Pseudomonas, Chlamydia, Bordetella, Treponema, Stenotrophomonas,Acinetobacter, Enterobacter, Klebsiella, Proteus, Serratia, Citrobacter,Enterococcus, Legionella, Mycoplasma, Chlamydophila, Moraxella,Morganella, and other human pathogens encountered in medical practice.Similarly, microorganisms may comprise fungi selected from a set ofgenera such as Candida, Aspergillus, and other human pathogensencountered in medical practice. Still other microorganisms may comprisepathogenic viruses (sometimes human pathogens) encountered in medicalpractice, including, but not limited to, orthomyxoviruses, (e.g.,influenza virus), paramyxoviruses (e.g., respiratory syncytial virus,mumps virus, measles virus), adenoviruses, rhinoviruses, coronaviruses,reoviruses, togaviruses (e.g., rubella virus), parvoviruses, poxviruses(e.g., variola virus, vaccinia virus), enteroviruses (e.g., poliovirus,coxsackievirus), hepatitis viruses (including A, B and C), herpesviruses (e.g., Herpes simplex virus, varicella-zoster virus,cytomegalovirus, Epstein-Barr virus), rotaviruses, Norwalk viruses,hantavirus, arenavirus, rhabdovirus (e.g., rabies virus), retroviruses(including HIV, HTLVI, and II), papovaviruses (e.g., papillomavirus),polyomaviruses, picornaviruses, and the like. With respect to viruses,in general, the methods and compositions of the disclosure may be usedto identify host cells harboring viruses.

As used herein, the term “microorganism” can be used to refer to asingle cell, such as a single or individual bacterial cell. The term“microorganism” may also be used to refer to a clone comprising morethan one cell, such as a group of cells or organisms produced asexuallyfrom a single progenitor cell or ancestor. In various embodiments and asused herein, the term “microorganism” may also refer to a group of cellsthat may be genetically distinct (i.e., arising from more than oneprogenitor cell or ancestor) but may be physically associated andevaluated as a single “microorganism.”

As used herein, the term “in association with a computer-based system,”and similar phrasing, in reference to any process step, process stepoutput (e.g., a growth curve), and the like (e.g., identification),means that it is either directly performed by the computer-based system,or based upon data, information, outcomes or reports from thecomputer-based system, or both. In reference to the term with otherprocess steps, inputs, and the like, the same might be either performeddirectly by the computer-based system, or performed in sequence to, orparallel with, the computer-based system.

Definition of Samples

The present disclosure provides systems of detecting microorganismswithin samples (e.g., sample 110 discussed herein). Samples, including,for example, samples in solution, may comprise any number of sources,including, but not limited to, bodily fluids (including, but not limitedto, blood, urine, serum, lymph, saliva, anal and vaginal secretions,perspiration, peritoneal fluid, pleural fluid, effusions, ascites,purulent secretions, lavage fluids, drained fluids, brush cytologyspecimens, biopsy tissue, explanted medical devices, infected catheters,pus, biofilms, and semen) of virtually any organism, with mammaliansamples, particularly human samples, and environmental samples(including, but not limited to, air, agricultural, water, and soilsamples) finding use in the system of the present disclosure. Inaddition, samples can be taken from food processing, which can includeboth input samples (e.g., grains, milk, or animal carcasses), samples inintermediate steps of processing, as well as finished food ready for theconsumer. The value of system of the present disclosure for veterinaryapplications should be appreciated as well, for example, with respect toits use for the analysis of milk in the diagnosis and treatment ofmastitis, or the analysis of respiratory samples for the diagnosis ofbovine respiratory disease.

Samples can range from less than a milliliter up to a liter for certainrespiratory lavage fluids, and can further range in bacterialconcentration from less than one bacterium to greater than 10⁹ bacteriaper milliliter. Furthermore, the sample can be present in blood, urine,sputum, lavage fluid or other medium. Sample concentration may be usedto concentrate the sample so that bacteria that are present in smallnumbers can be effectively introduced into the system, as well as so thebackground liquid medium can be normalized, or in some cases eliminatedor reduced, to provide consistent sample properties upon introduction toa system. It should be noted, however, that various samples may be usedwithout concentration or other modification within the scope of thepresent disclosure.

Determination of Microorganism Growth

In various aspects, a first value associated with an attribute of amicroorganism is determined by a computer-based system, based on firstinformation obtained from a microorganism detection system. A secondvalue associated with an attribute of the microorganism is determined bythe computer-based system based on second information obtained from amicroorganism detection system. A growth rate is determined by thecomputer-based system by reference to the first value and the secondvalue. In further aspects, a first value associated with a growth rateof a microorganism is determined by a computer system, based oninformation obtained from a microorganism detection system, optionallyin response to subjecting a microorganism to at least one of a firstevent and a first condition. As used herein, terms such as “determininggrowth,” “detecting growth,” “determining a growth rate” and similarvariations thereof may be used interchangeably with respect to variousaspects and embodiments of the present disclosure. The definitions of“growth” and other conceptually related terms are further defined below.

Definition of Growth

As used herein, the term “growth” may include any measurable changeattributable to or occurring within the life history of an organism,whether that change occurs under static external conditions or inresponse to a change in an internal or external event or condition. Theterm “growth” can be used to describe any change, regardless of whetherthe change is positive or negative. “Growth” can also be used todescribe a lack of growth, or neutral growth, where there may be nomeasurable change, or no net change, in a measured value of an attributeof a microorganism. “Growth” can be used to refer to one or more changesassociated with a single microorganism, or growth can be used to referto a net or collective change in a group, collection, or population oforganisms, whether derived from a single parental cell or from multipleparental cells. “Positive growth” in the case of microorganisms that arecells (e.g., bacteria, protozoa, and fungi) can refer to an increase inan attribute of a microorganism, including such attributes as, mass,cell divisions (e.g., binary fission events or cell doubling resultingin the production of daughter cells), cell number, cell metabolismproducts, or any other experimentally observable attribute of amicroorganism.

In accordance with various embodiments, microorganism size, (whether inone, two, or three dimensions), need not be used to evaluate growth withrespect to microorganisms that are individual cells or individuated,multicellular clones; in various embodiments, size may not be a usefulor informative metric of growth, and in fact may be misleading. However,in various embodiments and in accordance with a definition of“microorganism” wherein a microorganism may comprises a group orpopulation of cells originating from a plurality of unrelated progenitorcells, the size of the microorganism may be evaluated as an attributethat may be measured with respect to a determination of growth, insofaras the size of the microorganism may serve to represent the mass ornumber of the cells of which the microorganism is comprised. In the caseof viruses, “growth” can refer to the reproduction of viruses, generallywithin a host cell, and can include host cell lysis, in the case oflytic viruses. Thus, “growth” of a virus may sometimes be detected as aloss of the discrete host cell.

Various methods of detection of an attribute of a microorganism aredescribed in greater detail below. For example, detecting positivegrowth of a microorganism can include detecting either an increase inthe mass of the microorganism and/or detecting the occurrence of one ormore cell divisions as evidenced by the production of daughter cells. Invarious embodiments and as described in greater detail herein, detectionof a change in the mass of a microorganism may be performed using any ofa variety of methods that may directly or indirectly measure a change inthe quantity of mass of a microorganism.

As used herein, the term “mass” may be used in various senses withrespect to the measurement or detection of microorganism growth. Forexample, the term “mass” may be used in a formal sense to describe aquantity of matter, such as the mass of a microorganism or the change inthe mass of a microorganism as might be determined using a microbalance.In general, however, as used herein, the term “mass” may also be used todescribe a measure that may be indirectly or directly related to, orserve as a proxy for, a measurement of a quantity of matter. Forexample, a measurement of an increase in the size or number ofmicroorganisms may be described as an increase in the “mass” of themicroorganism(s) (i.e., the “biomass” of the microorganisms, such as maybe determined by various measurement techniques that assess, directly orindirectly, changes in an apparent “mass” of a microorganism, clone, orpopulation, where the measured change may or may not correlate exactlywith a change in the “mass” as an actual quantity of matter). Any methodthat may be used to evaluate the “mass” of a microorganism, whether by adirect determination of a quantity of matter, or by a measure of aquantity that may be directly or indirectly related to the quantity ofmatter of a microorganism, can be used to detect growth for purposes ofthe systems and methods of the present disclosure.

As mentioned herein, “detecting growth” can also refer to detecting alack of positive growth and/or to detecting negative growth. Forexample, a microorganism life cycle can include one or more phaseswherein “growth” may not be ascertainable using certain measurementtechniques, such as during periods traditionally referred to as a “lagphase” or “death phase.” Likewise, some antimicrobial agents act byretarding positive growth, while failing to produce cell mortality. Insuch cases, detecting little or no change in the size or mass of amicroorganism, for example, may be included within the evaluation of“growth.” Explained differently, in the absence of an antimicrobialagent, a microorganism may exhibit positive growth, but in the presenceof the agent, a lack of growth may be significant, even if themicroorganism does not die. Thus, in some cases, such as the forgoingexample, small (or decreased) changes may be measured in a period oftime relative to the mass, increase in number, or any other attribute ofmicroorganism that may be observed; however, these changes may bemeaningfully distinguishable from positive growth.

In addition, processes such as bacterial programmed cell death (e.g.,apoptosis and/or autophagic cell death) may be considered negativegrowth. In general, detection of negative growth relies on changes,usually but not always decreases, in microorganism mass, number, or anyother attribute that can be measured. In various embodiments, detectionof cell death may further include the use of an indicator of a conditionassociated with cell death or a lack of cell viability, such as a mortalstain or a change in intrinsic fluorescence.

As used herein, “detecting growth” can also refer to detecting changesin an attribute of a microorganism that may be in a growth phase otherthan that associated with logarithmic or exponential phase growth. Inother words, “detecting growth” can comprise detection of changes in anattribute of a microorganism in a phase of growth that mighttraditionally be referred to as lag phase, stationary phase, or deathphase, for example. Changes associated with such phases may be neutralor negative with respect to changes in the size, “mass,” or “biomass.”As described above, the lack of measurable changes in number, “mass,” or“biomass” may be significant and serves as a measure of “growth.”However, in various embodiments, other attributes that may nonethelessbe measured and exhibit measureable changes may also be used to derive ameasure of growth, as used herein. Thus, detection of growth can relateto measurement of attributes associated with, for example, homeostasis,catabolism, cell death, or necrosis. These attributes can include, forexample, measurement of metabolite production, protein production, cellmembrane integrity, ion channel activity, gene transcription, and thelike. Various attributes of a microorganism that may be measured withrespect to a determination of a growth rate, along with various methodsthat may be used to detect and measure those attributes, are describedin greater detail below.

Thus, “detecting growth” can refer to detecting positive growth,detecting a lack of growth, e.g., detecting cells that are not activelydividing but are not growing positively, and detecting negative growth,for example, cell death.

In various embodiments, detecting growth may be performed at theindividual or discrete microorganism level, rather than at a grosscolony or population level. Thus, “detecting growth of a discretemicroorganism” may be performed as an evaluation of growth of anindividual cell or clone, for example, in a period of time such that asmall population of daughter cells can be formed, but prior to theability to visually see a colony or population with the naked eye. Thisaspect of various embodiments has been described as “quantummicrobiology,” wherein individuated microorganisms (i.e., discretelyand/or repeatably identifiable microorganisms, whether individual cellsor clones comprising more than one cell, or microorganisms comprisingmore than clone in close physical approximate and treated as anindividual microorganism, as defined above) can be analyzed as describedherein. In various embodiments, devices such as biosensors,microfluidics chambers, microfluidics cartridges, and other specializeddevices may be used to facilitate microorganism individuation anddetection. Systems, methods, and devices that enable individuation anddetection of discrete microorganisms in accordance with variousembodiments of the present disclosure are described in detail in U.S.Pat. Nos. 7,341,841 and 7,687,239, which are herein incorporated byreference in their entirety.

In various embodiments, the ability to analyze or measure changes in theattributes of individuated microorganisms may, for example, enablegrowth to be detected in a short time frame in comparison to traditionalmicrobiological methods, such as minutes or hours rather than days.Similarly, growth may be detected within an absolute time frame of onlya few cell doubling events of a microorganism, rather than the tens orhundreds of doubling events that may be required to assess growth and/orsusceptibility with traditional methods. Furthermore, certainembodiments of the present disclosure do not require an initial growthof microorganisms (either liquid or solid) prior to an evaluation ofgrowth; rather, some methods are sufficiently sensitive to enablestarting with direct-from-specimen biological samples with no growth orculturing prior to the assay. In general, the methods of the disclosurecan be performed within and rely on measures of growth that may be madewithin an absolute timeframe within which a microorganism present in thesample under conditions suitable for growth may undergo from 1 to about10 doubling events, with from about 1 to about 4 being particularlyuseful, and 1 to 2 being ideal in situations where the “time to answer”is being minimized.

In various aspects, “detecting growth” may be performed using acomputer-based system, as described in greater detail below. In variousaspects, a computer-based system can comprise a processor,non-transitory memory, and an interface. The computer-based system maybe configured to perform method steps and/or execute instructions on acomputer readable medium or computer program. In these embodiments,detection of growth and/or a determination of a growth rate may be madeby integrating microorganism information associated with the detectionand/or measurement of one or more attributes of a microorganism over aperiod of time. In various embodiments, detection of growth and/or adetermination of a growth rate, or a lack thereof, for a microorganismneed not be based solely on a direct or absolute assessment of cellviability, change in size or mass, performance of metabolic processes(i.e., homeostasis, anabolic, or catabolic processes), reproduction, orthe like, but instead may be based on a probabilistic assessment that ameasured change in one or more attributes is likely to correspond togrowth. Thus, in various embodiments and as described in detail below,detection of growth and/or a growth rate may be determined based onmeasurement of a change in one or more attributes over time and adetermination of a statistical probability of whether the measuredchange corresponds to growth, as compared to a control or reference.

In various aspects, a method for detection of growth and/ordetermination of a growth rate may comprise determining, by acomputer-based system configured to analyze microorganism information,values associated with an attribute of a microorganism, based oninformation obtained from a microorganism detection system.Microorganism detection systems and/or methods that may be used toprovide microorganism information in accordance with variousembodiments, such as first information, second information, and thelike, are described in greater detail below.

In various aspects, a method may comprise determining, by thecomputer-based system, a first value associated with an attribute of amicroorganism based on first information obtained from a microorganismdetection system. A second value associated with an attribute of themicroorganism may be determined, optionally by the computer-basedsystem, based on second information obtained from a microorganismdetection system. A growth rate of the microorganism may be determinedby reference to the first value and the second value. The determinedgrowth rate may be compared to a control growth rate. In variousembodiments, the growth rates may be compared by the computer-basedsystem.

In various aspects, a first value associated with a growth rate of amicroorganism is determined by the computer-based system, based oninformation obtained from a microorganism detection system, optionallyin response to subjecting a microorganism to at least one of a firstevent and a first condition. A second value associated with a growthrate of a reference microorganism is obtained, optionally from amicroorganism detection system. In various aspects, a growth ratedetermined for a microorganism and a reference growth rate are compared.A proportional relationship between the first value and the second valuemay be compared by the computer-based system. In various embodiments, arelationship between a first value associated with a growth rate of amicroorganism and a second value associated with a growth rate of areference microorganism need not be compared by the computer-basedsystem.

As explained in more detail herein, a growth rate may be determinedbased on a statistical probability of whether a measured changecorresponds to growth may comprise a product of a plurality of factorsor likelihoods derived from an assessment of various factors. A controlagainst which an identification of growth (i.e., the statisticalprobability that a process interpreted as growth is occurring) is mademay be an internal control, such as a control run contemporaneously withthe sample for which a determination of growth is being made. In otherembodiments, a control may be a predetermined standard. For example, asample for which a growth rate is to be determined may be measured in anunder a set of standardized conditions for which one or more referencegrowth rates (for example, a library of reference or standard growthcurves) are available, and a determination of a growth rate may be madefor the sample based on comparison of the experimentally determinedgrowth rate against one or more reference growth rates. In variousembodiments, such reference growth rates may be empirically determinedby a user at a time that is separate from the experimental determinedgrowth rate for a sample. In other embodiments, reference growth ratesunder standard conditions for a microorganism detection system may bedetermined by a third party, and the reference growth rates may beobtained from the third party by a user for experimental determinationof a growth rate of a sample.

Determination of a growth rate, or a determination that a probabilitythat a measured change of an attribute of a microorganism in a samplecomprises growth in a statistically or clinically meaningful sense, maybe made for a sample based not only on comparison of a sample growthrate against one or more reference growth rates, but may also compriseconsideration of additional factors that may modify the calculation ofthe probability of growth based on comparison of the measured attributeto a reference growth rate. For example, measurements of an increase incell volume may be used to determine a growth rate, but those samemeasurements of cell volume may also be fit to models or referencesrelative to models of cell shape that may or may not be integrated intothe reference growth rates used for comparison. In this manner,conformity (or lack thereof) of attributes of cell size and/or shapechanges relative to various expected models of cell morphology maymodify the probability that the measured microorganism attribute changecorresponds to a reference growth rate. Similarly, other factors, suchas the number of cell divisions and the probability that the observedcell or group of cells in a measurement made at a first time correspondsto the observed cell or group of cells in measurement made at a secondor subsequent times may also modify to determination of the probabilitythat a sample or microorganism therein is demonstrating growth.

In various embodiments, a determination of a growth rate of amicroorganism in response to a condition may provide clinicallymeaningful or useful information, for example, where the microorganismoriginates from a patient sample and the condition comprises anantimicrobial agent. The growth rate determined for a microorganism,compared against a reference growth rate, in accordance with variousembodiments of the present disclosure, may facilitate identifyingwhether the microorganism is susceptible to the antimicrobial agentand/or whether the microorganism is resistant to the antimicrobialagent. For example, susceptibility of a microorganism to anantimicrobial agent may be identified by determining a rate of growth inresponse to a condition comprising a concentration of the antimicrobialagent and comparing the growth rate of the microorganism to a referencegrowth rate. If a proportional relationship of the growth rates isdetermined to be outside of a reference range or below a thresholdcriterion, for example, the microorganism may be identified assusceptible to the antimicrobial agent.

As used herein, “susceptibility” includes instances in which anantimicrobial agent has an inhibitory effect on the growth of amicroorganism or a lethal effect on the microorganism. “Susceptibility”further includes the concept of a minimum inhibitory concentration(“MIC”) of an antimicrobial agent, defined as a concentration of anantimicrobial agent that will arrest growth of a microorganism.Identification of “susceptibility,” or a lack of susceptibility forexample, using the system and method described herein, may provideinformation that may be useful to a clinician in making a decisionregarding antimicrobial agent therapy for a patient.

In various embodiments, for example, if a proportional relationship ofgrowth rates is determined to be outside of a reference range or above athreshold criterion, a microorganism may be identified as resistant.

As used herein, “resistant” includes instances in which a microorganismis not substantially affected by an antimicrobial agent. For example,resistance may be identified by determining that a microorganism'sgrowth is not substantially affected by a MIC of an antimicrobial agent.It is important to note that identification of resistance provides noinformation as to susceptibility or identification of clinicallytherapeutic treatment options, with the exception that the antimicrobialagent to which a microorganism is identified as resistant may be ruledout as a viable clinical therapeutic agent, assuming that the resistantmicroorganism is a pathogenic microorganism responsible for an infectionin a patient. In various embodiments, “resistance” may be related toknown mechanisms associated with a particular microorganism genotype,wherein the mechanism providing antimicrobial agent resistance to themicroorganism is genetically encoded and expressed to confer a resistantphenotype.

In various embodiments, determination of a growth rate may alsofacilitate identifying whether a microorganism demonstrates intermediatesusceptibility to an antimicrobial agent. Traditional clinical ASTtesting procedures often report information related to the identity of apathogen in a sample and a table of antimicrobial agents to which apathogen or other microorganism is susceptible, intermediate, orresistant (“SIR”). In accordance with various embodiments, the systemand methods disclosed herein may be used to rapidly provide similarinformation, including empirically determined resistance andsusceptibility data based on subjecting a microorganism to a conditioncomprising an antimicrobial agent, rather than data that may simply relyon performing microorganism identification alone.

In various embodiments, determination of the growth rate of amicroorganism may further be used to identify that the microorganism is,for example, expressing a virulence factor or hypervirulent. Forexample, a microorganism may demonstrate an altered growth rate due toexpression of one or more virulence factors that may be associated withenhanced pathogenicity. In various embodiments, a growth rate of amicroorganism expressing a virulence factor (or a hypervirulentmicroorganism) may exhibit a growth rate that is higher than a referenceorganism, or a growth rate that is lower than a reference organism.

Microorganism Detection Definition of an Attribute of a Microorganism

As used herein, an “attribute of a microorganism” can be any detectableor measurable feature or characteristic of a microorganism. An attributecan be directly associated with a microorganism, for example, a featureor characteristic that is physically located on or within or otherwisedirectly physically associated with a microorganism. Such a directlyassociated attribute can include, for example, the size, shape, mass,intrinsic fluorescence, cell surface features, membrane integrity,genomic DNA, ribosomal RNA subunits, etc. A directly associatedattribute can also include, for example, both directly and indirectlybound markers such as may be used in indirect immunofluorescencemicroscopy. As used herein, an attribute can also include a feature thatis indirectly associated with a microorganism, such as proteins, ions,osmolytes, metabolites, or any other chemical or macromolecularsubstance that may be secreted, released, or exchanged into a medium anddetected directly or indirectly. For example, colorimetric indicatorsmay be used as substrates in enzyme-linked immunosorbent assays.Furthermore, an attribute can be a feature or characteristic of amicroorganism, regardless of whether tne microorganism is viable ordead, intact or disrupted. Any value related to the presence of amicroorganism that may be observed, detected, or measured, using anytechnique, whether presently available or yet to be developed, is withinthe scope of an attribute of a microorganism as used in the presentdisclosure.

Growth Conditions

In various embodiments, microorganisms to be analyzed may be maintainedin or subjected to one or more conditions suitable for growth. Forexample, a microorganism detection system may include one or more samplevessels in which microorganism samples are placed for detection andanalysis. In various embodiments and as illustrated in FIGS. 4A and 4B,a sample vessel may comprise a disposable microfluidic flowcell cassettehaving a plurality of separate cells (flowcells) or chambers into whichmicroorganisms may be placed. The cassette and the flowcells may beconfigured, for example, as described in U.S. Pat. No. 7,341,841, thecontents of which are incorporated herein by reference, in a mannerwhereby it may be possible to regulate the media composition (includingantibiotic presence and concentration), flow rate, temperature, pH, gasmixture, pressure, and any other environmental parameter that may beregulated in relation to microorganism growth. Likewise, the remainderof the system outside of the cassette may also be configured to providefor regulation and/or manipulation of any of a variety of environmentalor external parameters.

In various embodiments and as used herein, a “condition” can be anyparameter related to or having an influence on microorganism growth. Forexample, a “condition” can include parameters required for or beneficialto microorganism growth, and a “condition” can include parameters thatmay inhibit microorganism growth. Likewise, a “condition” can refer to asingle parameter or variable that may influence microorganism growth, ora “condition” may collectively refer to a set of parameters orvariables. In accordance with various embodiments, a “condition” maysimply refer to the passage of time. A “condition” can include anytraditional microbiological culture medium that may be known to a personof ordinary skill in the art, and a “condition” can further include anygrowth (or selective) medium comprising any combination of mediumcomponents, whether defined or undefined (complex). Examples of mediumcomponents and classes of components include carbon sources, nitrogensources, amino acids, extracts, salts, metal ions, cofactors, vitamins,dissolved gasses, and the like. Similarly, a “condition” can includevarious components that might be added to a medium to influence thegrowth of a microorganism, such as selective and non-selectiveantimicrobial agents that may inhibit or arrest microorganism growth,modulating agents (i.e., agents that may alter microorganism growth butare not considered antimicrobial agents), or enrichment agents (e.g.,substances that may be required for auxotrophic microorganisms, such ashemin, or substances that may be required by fastidious organisms) orother components that may encourage microorganism growth. A “condition”can also include other environmental parameters separate from thecomposition of a culture medium, such as light, pressure, temperature,and the like. Similarly, a condition can include any of a variety ofother parameters that might occur or be imposed, such as: a hostorganism defensive material or cell (e.g., human, defensin proteins,complement, antibody, macrophage cell, etc.), a surface adherentmaterial (i.e., surfaces intended to inhibit growth, kill cells, etc.),a physiological, metabolic, or gene expression modulating agent (e.g.,host defense activation with co-cultured host cells), a physiologicalsalt, metabolite, or metabolic waste materials (such as may be producedby living microorganisms or used to simulate late-stage culture growthconditions (i.e., stationary phase conditions)), a reduction in nutrientmedia (simulating, for example, stationary phase conditions), or abacteriophage infection (actual or simulated). Furthermore, a“condition” may be static (e.g. a fixed concentration or temperature) ordynamic (e.g. time-varying antimicrobial concentration to simulatepharmacokinetic behavior of intermittent infusions; or to simulate anyendogenous or exogenous process affecting microbe response). Thesedefinitions of “condition” are intended to be illustrative, rather thanexhaustive, and, as used herein, a “condition” can include anyendogenous or exogenous parameter that may influence a microorganism.

In various embodiments, a system may include a temperature regulatedincubation chamber in which the sample cassette may be maintained duringmicroorganism detection and analysis. In various embodiments, a systemmay include ability to provide for temperature regulation of thecassette or sample chambers such as by using Peltier elements, resistiveheating elements, or temperature regulation of circulating liquid mediumfor growth during or between evaluations. Temperature regulation maycomprise maintaining a microorganism at a fixed temperature during ananalysis period, or may comprise providing changing temperaturesaccording to a predetermined temperature profile. A temperature regimencomprising changing temperature can include temperature changes atpredetermined temperature change slopes or ramps. In variousembodiments, temperature regulation may comprise simultaneouslyproviding different temperatures or temperature profiles for individualchambers or flowcells in a cassette during an analysis period.

In some variations the system may be further configured to acceleratemicroorganism (and particularly bacterial) growth relative to standardclinical microbiological culturing conditions. Microorganism growth maybe accelerated while evaluating growth by changing, for example, thetemperature, composition, and/or oxygen content of the media. Forexample, increasing the temperature may provide an increased rate ofmicroorganism growth and enable a determination of a rate of growthusing the system and method disclosed herein in a shortened time framerelative to incubation at a temperature used in standard AST methods.

Microorganism Detection System

In various embodiments, microorganisms in a sample or specimen areintroduced into a microorganism detection system. A microorganismdetection system may comprise a specialized device to facilitatemicroorganism individuation, growth, and/or detection. In variousembodiments, a specialized device for microorganism individuationcomprises, for example, a biosensor or a disposable cartridge such asthose described in U.S. Pat. Nos. 7,341,841 and 7,687,239.

In various embodiments, a multiplexed automated digital microscopy(MADM) microorganism detection system comprises a computer-based systemand may be a bench top instrument that combines a disposable fluidiccartridge with automated microscopy and image analysis software. TheMADM system can include, among other features, automated sampledistribution to multiple on-board analysis chambers providing integratedelectrokinetic concentration (EKC) and imaging, electrophoreticconcentration to a capture and imaging surface using transparent indiumtin oxide (ITO) electrodes and redox buffer system, phase contrast,darkfield, and fluorescence microscopy, XYZ motion control includingautofocus, off-board (instrument-based) pumps and fluid media, on-boardreagent reservoirs (antibodies, stains, antibiotics), and activeon-device valving for fluidic network control.

Evaluations can be performed using the system, with off-board specimenpreparation (i.e., simple centrifugation or filtration samplepreparation). The MADM system can provide rapid concentration ofbacteria to assay capture and imaging surface using electrokineticconcentration. Targeted bacterial identification can be performed byfluidic introduction of species specific antibodies followed byfluorescently labeled secondary antibodies, with automatedepi-fluorescent microscopy. In various embodiments, individual clonescan be mapped, and growth rate determination exploits registeredtime-lapse image analysis, processed to derive growth rate information(e.g., doubling times and growth rate constants). The MADM system canalso provide on-board, near real-time antibiotic susceptibility testing(AST).

In various embodiments, a flowcell for use with a microorganismdetection system can include indium tin oxide (ITO; conductive andtransparent) coated glass as top and bottom layers, optionally with anadsorptive chemical coating on the bottom surface. A sample containingmicroorganisms may be introduced and a potential applied. Since bacteriaare generally negatively charged, they migrate to the positively chargedsurface, where they may adsorb to the chemical coating. Afterelectrokinetic concentration, the device may be automatically filledwith growth media (TSB). All subsequent assay steps may be performed inmedia and microorganism viability may be maintained throughout theprocess.

Once the microorganisms present in the sample have been individuated,individual microorganisms can be interrogated (e.g., optically,spectroscopically, bioelectroanalytically, etc.) using the microorganismdetection system to measure an attribute of, characterize, and/oridentify the microorganisms in the sample. The interrogation ordetection of an attribute of a microorganism can take place in anysuitable manner, including a non-invasive manner that does not interferewith the integrity or viability of the microorganism, that is,attributes of a microorganism present in a sample can be detected andmeasured while the microorganism remains in the sample cassette and/orremains intact. Moreover, in various embodiments, attributes of amicroorganism may be detected while the organism remains viable and/orcapable of undergoing growth. The ability to identify the microorganismsin a non-invasive manner, optionally coupled with keeping the samplecontained (e.g., sealed within a sample cassette or equivalent device)throughout the analysis process, along with automation of the procedure,may contribute to reduced handling of potentially pathogenic samples andmay increase the safety of an identification or AST process relative totraditional clinical microbiological methods. Furthermore, the abilityto characterize and/or identify microorganisms, for example, by directinterrogation of a direct-from-specimen sample without furtherprocessing of the sample (e.g., resuspension, plating, and growth ofcolonies) can greatly increase the rapidity with whichidentification/characterization can be made.

Any of a number of detection systems and/or methods that may provide anability to detect an attribute of a microorganism may be used inaccordance with various aspects and embodiments. In some embodiments,systems and/or methods that may provide real-time or near real-timedetection are used. These include brightfield imaging, darkfieldimaging, phase contrast imaging, fluorescence imaging, upconvertingphosphor imaging, chemiluminescence imaging, evanescent imaging, nearinfra-red detection, confocal microscopy in conjunction with scattering,surface plasmon resonance (“SPR”), atomic force microscopy, and thelike. Likewise, various combinations of detection systems and/or methodsmay be used in parallel or in complementary fashion to detect one ormore attributes of a microorganism in accordance with the presentdisclosure.

Spectroscopic methods can be used to detect one or more attributes ofthe microorganisms. These may include intrinsic properties, such as aproperty present within the microorganism in the absence of additional,exogenously provided agents, such as stains, dyes, binding agents, etc.Optical spectroscopic methods can be used to analyze one or moreextrinsic attributes of a microorganism, for example, a property thatcan only be detected with the aid of additional agents. A variety oftypes of spectroscopy can be used, including, for example, fluorescencespectroscopy, diffuse reflectance spectroscopy, infrared spectroscopy,terahertz spectroscopy, transmission and absorbance spectroscopy, Ramanspectroscopy, including Surface Enhanced Raman Spectroscopy (“SERS”),Spatially Offset Raman spectroscopy (“SORS”), transmission Ramanspectroscopy, and/or resonance Raman spectroscopy or any combinationthereof.

Spectroscopic detection can be carried out by any technique known tothose of skill in the art to be effective for detecting and/oridentifying one or more intrinsic or extrinsic attributes of amicroorganism. For example, front face fluorescence (where theexcitation and emitted light enters and leaves the same optical surface,and if the sample is generally optically thick, the excitation lightpenetrates a very short distance into the sample and can be used foridentification of microorganisms. Other forms of measurement, such asepifluorescence, reflectance, absorbance, and/or scatter measurements,can also be employed.

Typically, the light source, or excitation source, results in theexcitation of the sample, followed by measurement of the emission offluorescence of the sample at predetermined time points or continuously.Similarly, the reflected light from interaction of the excitation sourcewith the sample may be measured to provide pertinent data foridentification and/or characterization. The emission from the sample maybe measured by any suitable means of spectral discrimination, such as byemploying a spectrometer.

A sample illumination source, or excitation source, may be selected fromany number of suitable light sources as known to those skilled in theart. Any portion of the electromagnetic spectrum that produces usabledata can be used.

Detection systems and/or methods may be used that rely on fluorescencesignal (e.g., intrinsic fluorescence or fluorescence due to the presenceof added indicator dyes) due to excitation by a UV, visible spectrum, orIR light source. The light sources can be continuum lamps such as adeuterium or xenon lamps for UV and/or a tungsten halogen lamp forvisible/IR excitation. Since these light sources have a broad range ofemission, the excitation band can be reduced using optical bandpassfilters. Other methods for emission wavelength spectral width that maybe utilized include an acousto-optic tunable filter, liquid crystaltunable filter, an array of optical interference filters, prismspectrograph, and the like. Alternatively, lasers are available indiscrete wavelengths from the ultraviolet to the near infra-red. Any ofa variety of fluorescence signal-based multiplexing methods will beknown to those skilled in the art and are within the scope of thepresent disclosure.

Alternatively, light-emitting diodes can be used as narrowbandexcitation light sources. LED's are available from a peak wavelength of240 nm to in excess of 700 nm with a spectral width of 20-40 nm. Thesame methods for the reduction of spectral width can be incorporatedwith the LED's to improve discrimination between excitation and emissionspectra. In various embodiments, a plurality of narrowband lightsources, such as LEDs or lasers, may be spatially and/or temporallymultiplexed to provide a multi-wavelength excitation source.

The emission from the sample may be measured by any suitable means ofspectral discrimination, most preferably employing a spectrometer. Thespectrometer may be a scanning monochromator that detects specificemission wavelengths whereby the output from the monochromator isdetected by a photomultiplier tube and/or the spectrometer may beconfigured as an imaging spectrograph whereby the output is detected byan imaging detector array such as a charge-coupled device (“CCD”) cameraor detector array. In one embodiment, a discriminator allows theobservation of the fluorescence and/or scattering signal by aphotodetection means (such as a photomultiplier tube, avalanchephotodiode, CCD detector array, a complementary metal oxidesemiconductor (“CMOS”) area sensor array and/or electron multiplyingcharge coupled device (“EMCCD”) detector array. Fluorescence signalstrength at several different wavelengths are acquired and saved in acomputer memory.

The detection of growth could also be accomplished using Ramanspectroscopy. Raman spectroscopy is a non-contact technique where thesample is illuminated by laser radiation. The scattered light is eitherelastically or inelastically scattered by interaction with the moleculeswhich comprise the microorganism. The elastically scattered light isreferred to as Rayleigh scattering and the inelastically scattered lightis Raman scattering. Raman spectroscopy has been shown to be apotentially viable method of microorganism identification and/orcharacterization by examination of the vibrational spectra of themicroorganism.

The laser illumination and scattering collection optics are designed tofocus the beam to a near-diffraction limited spot size. This sizeensures adequate laser signal on the microbe since Raman scattering isvery inefficient. The collection optics are designed to efficientlycapture scattered light and couple it into an optical spectrometer foranalysis. The Raman signal can be acquired at one or more locations andthe subsequent signal averaged.

Once Raman spectra are obtained, they may be analyzed for location andstrength of key peaks in the spectra. This data may be compared to astored reference data set of known microorganisms so that determinationsof, for example, Gram type, morphological information, and speciesidentification, can be obtained. A reference data set from knownmicroorganisms can be obtained in the system and methods describedherein, or may be obtained from a third party.

To enhance Raman (SERS) and fluorescence signals, microorganisms couldeither be coated with gold and/or silver nanoparticles in a samplepreparation step, and/or the inner optical surface could be pre-coatedwith metal colloids of particular size and shape. In variousembodiments, the nanoparticles may be associated with microorganisms ina centrifugation step.

Spectra such as fluorescence spectra obtained using various methodsdescribed above may be used to perform identification of microorganisms.Reference spectra may be obtained for known microorganisms, thusallowing for correlation of measured sample data with characterizationof the microorganisms of interest using various mathematical methodsknown to those skilled in the art. The measured test data from knownmicroorganisms is stored in machine-readable memory, e.g., within theinstrument itself or within an associated data processing device, suchas a connected computer-based system. For example, the data from samplesbeing tested by the instrument may be compared with the baseline orcontrol measurements utilizing software routines known to or within theability of persons skilled in the art to develop. More particularly, thedata may be analyzed by a number of multivariate analysis methods, suchas, for example, General Discriminant Analysis (“GDA”), Partial LeastSquares Discriminant Analysis (“PLSDA”), Partial Least Squaresregression, Principal Component Analysis (“PCA”), Parallel FactorAnalysis (“PARAFAC”), Neural Network Analysis (“NNA”) and/or SupportVector Machine (“SVM”). These methods may be used to classify unknownmicroorganisms of interest in the sample being tested into relevantgroups (e.g., species) based on existing nomenclature, and/or intonaturally occurring groups based on the organism's metabolism,pathogenicity and/or virulence in designing the system for evaluating,detecting and/or characterizing the organism as described herein.

Microorganisms associated with a detection system can be interrogatedusing mass spectrometry techniques, such as MALDI-TOF mass spectrometry,desorption electrospray ionization (“DESI”) mass spectrometry, GC massspectrometry, LC mass spectrometry, electrospray ionization (“ESI”) massspectrometry and Selected Ion Flow Tube (“SIFT”) spectrometry.

A bioelectroanalytical microorganism detection system may be used todetect and measure the mass increase of individual microorganisms innear real time. Such a system can include, for example, a systemconfigured to measure impedance or to perform electrochemical impedancespectroscopy. In accordance with various embodiments, the system canprovide a rapid and accurate evaluation of the growth dynamics for thepopulation of viable organisms in the sample.

A bioelectroanalytical quantitative growth measurement system maycomprise a sample device, such as a disposable microfluidic cartridgewith a surface having an array of discrete, individually addressableelectrodes suitable for performing impedance measurements,electrochemical impedance spectroscopy, or other bioelectroanalyticalmeasurements. In various embodiments, a microelectrode or ananoelectrode array may be used. An opposing plane of the microfluidicscartridge may likewise comprise an electrode or an electrode array. Invarious embodiments, microorganisms introduced into a sample device maybecome physically associated with a surface or location of the sampledevice. For example, microorganisms may be electrokineticallyconcentrated to a surface of a microfluidic cartridge prior toperforming bioelectroanalysis. In various other embodiments,microorganisms introduced into the cartridge or other device may beassociated with a surface of the device and/or confined at a discretelocation of the device (e.g., an addressable location on a planarsurface or a discrete, recessed well) using other forms of passive oractive cell movement, such as settling, fluid flow, cell trapping,centrifugation, etc. The array of micro- or nanoelectrodes may be usedto measure charges associated with a microorganism cell wall and/or therelease of ions or other osmolytes from microorganisms in the cartridge.In various embodiments, the electrode size combined with the sensitivityand dynamic range of the bioelectroanalytical measurement system may besuitable to respond proportionately to the mass of viable microorganismstructure adjacent to the electrode. For example, the sensitivity of anindividual bioelectroanalytical sensor may be suitable to detect ionsreleased or exchanged into the medium by a metabolically activemicroorganism adjacent the sensor, while a neighboring sensor moredistant from the cell detects a smaller ion concentration due toincreased diffusion of ions with increased electrode or sensor distance.

For example, an electrode located near the center of mass of a cell orclone may provide a greater bioelectroanalytical response or measurementvalue than an electrode located adjacent the edge of a cell or otherwiseonly partially occupied by or in proximity with a cell. A series ofmeasurements, near continuous measurements, or continuous measurementsmay be taken at each electrode in an array over time, and the system maybe suitable to obtain and record frequent or near real timemeasurements. In this manner, a bioelectroanalytical measurement systemmay provide data obtained from multiple discretely addressed electrodesfor a given microorganism. Likewise, the system may obtain, for example,thousands of discretely addressed electrode measurements at each time inan experimental time course. The data output may thus resemble opticalimage data comprising discrete pixels with unique addresses, eachelectrode registering a resistance that may vary within a significantdynamic range dependent proportional to the presence of live and/orgrowing microorganisms. The data acquired by a bioelectroanalyticalmeasurement system may be processed in accordance with the processesperformed by analysis module 140 as described in detail herein withrespect to image data.

A variety of other microorganism detection systems and/or methods havebeen used to detect and/or determine values associated with variousattributes of a microorganism, including, for example, optical density,nephelometry, densiometry, flow cytometry, capillary electrophoresis,analytical chemistry and indicator-based methods of metabolitedetection, protein output, molecular diagnostics, quartz crystalmicrobalance, bioluminescence, microcantilever sensors, and asynchronousmagnetic bead rotation, among others, and are also included within thevarious aspects and embodiments.

Of the various approaches that have been described herein, some, such asvarious optics-based methods, impedance, surface plasmon resonance, andatomic force microscopy, are compatible with non-destructive measurementor detection of individual, living microorganisms and can be used toevaluate microorganism growth and/or development of a multicellularclone. Some of these methods are furthermore capable of resolving andproviding multiple measures or data points for a particular,individuated microorganism at any given point in time. Any method, asmay be currently in practice or developed in the future, may be used todetermine a value associated with an attribute of a microorganism foruse in determining a growth rate, as disclosed herein.

Analysis

In various aspects and embodiments, and with reference to FIG. 1, acomputer-based system 100 may comprise a sample 110, and input system120, a sample analyzer 130, an analysis module 140, and/or a healthcareinformation system (“Healthcare IS”) 150. Input system 120 and sampleanalyzer 130 may together comprise a microorganism detection system. Amicroorganism detection system may further comprise additionalcomponents. System 100 may be configured to evaluate individualmicroorganisms, collect data associated with the individualmicroorganisms in response to events, analyze the data, and determine anattribute of the microorganism. An example of a computer-based system100 is the multiplexed automated digital microscopy (MADM) systemreferred to in several examples of the present disclosure, variousfeatures of which are described in more detail herein.

In various aspects and embodiments, sample 110 may be any suitablebiological sample containing a microorganism. For example, sample 110may be a biological fluid (e.g., blood or other bodily fluid), alaboratory specimen from a culture, or any other suitable samplecontaining a microorganism. Sample 110 may be collected from a patientin a healthcare setting. Moreover, sample 110 may be collected fordiagnostic, treatment, scientific, or any other suitable purpose.

In various aspects and embodiments, input system 120 may be any systemcapable of receiving, processing, handling, dispersing and/or otherwisepreparing a sample. Input system 120 may comprise a sample input capableof receiving samples 110 from any suitable source (e.g., a vial, a testtube, a culture, an assay, and/or the like). Input system 120 may beoperatively coupled to sample analyzer 130. More specifically, inputsystem 120 may comprise a distribution system capable of preparing androuting samples to a sample analyzer 130. The distribution system maycomprise a manifold capable of receiving a plurality of samples. Thedistribution system may also comprise one or more pumps and plumbing toroute the plurality of samples to the sample analyzer. Input system 120may be capable of processing and/or preparing sample 110 prior to,during, or after transport of sample 110 from the distribution system tosample analyzer 130.

In various aspects and embodiments, sample analyzer 130 may be anyhardware, software, or hardware-software system capable of evaluatingand collecting data about sample 110. Sample analyzer 130 may compriseany suitable microorganism evaluation, measuring, and data collectiondevices. Sample analyzer 130 may comprise any instrument or be capableof performing any evaluation and/or data collection process, steps,and/or method described herein with respect to microorganism detection.More specifically, sample analyzer 130 may be capable of detecting,evaluating, characterizing, or otherwise analyzing one or moreindividuated microorganisms.

In various embodiments, analysis module 140 may be any hardware,software, or hardware-software system capable of evaluating andcollecting data about sample 110. Analysis module 140 may be operativelycoupled and/or in electronic communication with sample analyzer 130. Thefunctions performed by analysis module 140 may be performed using anycombination of hardware, including, for example, a computer-basedsystem, a special purpose computer, a general-purpose computer, adistributed computer system, a consolidated computer system, or a remoteserver or computer-based system. Analysis module 140 may be capable ofreceiving and processing microorganism information from sample analyzer130. For example, analysis module 140 may be capable of receiving imagedata associated with a microorganism. The image data may represent oneor more individuated microorganisms, populations of individuallyidentifiable microorganisms, other information associated with a sample,information regarding debris and noise, and any other suitableinformation collected, analyzed and/or received by sample analyzer 130.Moreover, analysis module 140 may be operatively coupled and/or inelectronic communication with input system 120. Analysis module 140 mayreceive patient and/or sample data from input system 120 and/or sampleanalyzer 130 (e.g., information indicating the source of the sample,characteristics of the sample, sample collection information, and/or thelike).

Analysis module 140 may be further capable of processing and/oranalyzing the microorganism information. For example, analysis module140 may be capable of parsing the information, assess variouscharacteristics of a microorganism (e.g., location, growth rate, mass,doubling, and/or the like). Analysis module 140 may also be capable ofidentifying parsed data that is not indicative of or associated with amicroorganism (e.g., background, debris, noise, and/or the like).Further, analysis module 140 may be capable of associating variouscharacteristics of one or more microorganisms with events and/orconditions. For example, analysis module 140 may be configured toevaluate a growth rate of an object and/or object site over time.Analysis module 140 may also evaluate multiple events and/or conditionsover time. For example, analysis module 140 may be capable of evaluatinga growth rate over time and associated with growth rate with specificevents or conditions (e.g., the introduction of heat, the introductionof an antimicrobial agent, and/or the like).

The multivariable analysis capability of analysis module 140 alsoprovides computer-based system 100 with an ability to make arecommendation or determination about a microorganism based on one ormore events or conditions. For example, based on a change or a lack ofchange of a growth rate in response to an event or condition (e.g., theintroduction of an anti-microbial agent), analysis module 140 may becapable of determining a characteristic (e.g., susceptibility to theantimicrobial agent) or the identity of the microorganism. As will bedescribed in greater detail herein, analysis module 140 may evaluate andcharacterize the changing or lack of change of the microorganisminformation and render a recommendation or determination of themicroorganism characteristic.

In various embodiments, healthcare IS 150 may be any hardware, software,or hardware-software system capable of evaluating, receiving,processing, associated, and/or displaying microorganism informationabout sample 110. Healthcare IS 150 may be operatively or electronicallycoupled to analysis module 140 and/or any other component of system 100.Healthcare IS 150 may comprise one of more portals that are accessibleto a healthcare provider. For example, Healthcare IS 150 may comprise anelectronic medical record (“EMR”) or other suitable patient health datamanagement system that is capable of providing information andrecommendations about a patient's condition.

In various embodiments and with reference to FIGS. 3-4, system 100 maybe a computer-based system for individuated microorganism detection,tracking and analysis. System 100 may be capable of event or conditiontriggered (e.g., time-lapsed) microorganism information capture.

In various embodiments, computer-based system 100 may detect, measure,track, and analyze individuated microorganisms based on optical imagedata, such as digital photomicrographs acquired using any of a varietyof methods and imaging modes well known to a person of skill in the art,various examples of which are further described below. System 100 maymeasure microorganism attributes and perform data analysis usingmeasured signal intensity values, such as, for example, pixel intensityvalues from a digital image. In various embodiments, non-optical methodsmay be used for detection, data acquisition, and analysis, and any formof quantitative data or measured signal intensity values that may beacquired by any of a variety of measurement systems may be suitable foranalysis by system 100. In various embodiments, microorganisminformation acquired by a non-optical method may be processed in amanner similar to that described in detail herein with respect to pixelintensity values derived from image data.

In various embodiments, system 100 is fully automated and capable ofhandling various noise levels and signal intensity ranges, independentof illumination heterogeneities, and applicable to differentindividuated microorganism evaluation systems and methods (e.g., imagingmodes, including dark-field, fluorescent, and phase contrast images,and/or the like). For example, locally determined background signalintensities may be used to compensate for illumination heterogeneitiesthat may be introduced as a function of irregular illumination intensityfrom the illumination source or an irregular interaction of lightemitted from the illumination source with the sample cassette.

Examples of image properties that make microorganism detection and clonetracking non-trivial include: high and varying levels of noise, unevenillumination or background signal from a signal source, large amount ofdebris, and non-immobilized microorganisms.

In various embodiments, system 100 is capable of detecting individuatedmicroorganisms. System 100 may be capable of performing, for example,method 200 and/or method 300.

In various embodiments and with reference to FIG. 2, system 100 may becapable of evaluating microorganisms. A sample containing microorganisms(e.g., a sample from a patient, a control sample, a research sample,and/or the like) for evaluation may be provided to any suitablecomponent of system 100 (Step 210). The sample may be processed orprepared for evaluation in any suitable manner such as, for example,with input system 120. The sample may then be provided to sampleanalyzer 130. As described herein, sample analyzer 130 may be capable ofdetecting any attribute of a microorganism and/or any sample element. Asused herein, the terms “sample element” and “element” includemicroorganisms, contaminants, debris, and/or the like. In response tothe sample being prepared and received by sample analyzer 130, sampleelements may be analyzed or identified.

This analysis may include the evaluation of any suitable attribute ofelements of the sample. In this way, sample analyzer 130 may evaluateand measure, characterize, sense or otherwise quantify one or morephysical or non-physical attributes of elements of a sample to determineelement information. System 100 may process and/or quantify theseattributes in any suitable fashion. For example, system 100 may assign afirst value to the each attribute or each element (Step 220). This firstvalue may be a different first value for each detected, identifiedand/or analyzed attribute of each microorganism.

The first value may be initially analyzed against a predetermined ordynamically determined reference range. This reference range may beassociated with an attribute to be measured. In response to theattribute being within the reference range, system 100 may identify theelement associated with the attribute as an element to be monitored. Inresponse to the attribute being outside the reference range, system 100may determine that the element associated with the attribute is anelement that is not monitored.

In various embodiments, the sample and/or one or more elements may besubject to a condition (Step 230). The condition may be any suitablecondition described herein. The condition may be introduced at anysuitable time. For example, the condition may be introduced as part ofsample preparation. The condition may also be introduced in response toand/or at a time or event after system 100 has identified or isevaluating the elements. The condition may also be introduced to theentire sample or to one or more portions of the sample.

In various embodiments, system 100 may evaluate the elements in responseto and/or based on the condition. System 100 may be capable ofcollecting and or associating element information with the condition.Moreover, system 100 may be capable of collecting and associatingelement information based on or as a function of the condition. Forexample, the condition may be an exposure to a specific substance ortime. In this example, system 100 may be capable of evaluating,correlating, and storing information about element attributes (orchanges in an element's attributes in response to the condition and/ortime). In this way, system 100 may be capable of determining a secondvalue associated with an element attribute in response to an event (e.g,time) (Step 240). The second value may characterize a change or nochange in an attribute and/or the element. Like with the first value,the second value may be a different second value for each detected,identified and/or analyzed attribute of each element. Moreover, thesecond value may be a function of the first value and/or proportionallyassociated with the first value. As such, the second value, whencompared to or analyzed with the second value may describe the nature ofchange of the first value. For example, system 100 may use the firstvalue and the second value to determine a rate of change of an attribute(e.g., a grown rate of an element). Moreover, system 100 may use thesecond value or a comparison of the first value and the second value todetermine whether an element is an element of interest (e.g., amicroorganism).

The second value may also be analyzed against a second predetermined ordynamically determined reference range. This second reference range maybe associated with the attribute being measured. In response to theattribute being within the second reference range, system 100 mayidentify the element associated with the attribute as an element to beanalyzed or evaluated. In response to the attribute being outside thesecond reference range, system 100 may determine that the elementassociated with the attribute is not relevant for further analysis orevaluation (e.g., the element can be discarded or ignored).

In various embodiments, system 100 may be capable of assessing theresponse of the element to a condition (Step 250). For example, system100 may determine a rate of change (e.g., a growth rate of an element)in the presence of the condition and in response to the event, based onthe first value and the second value. This rate of change of anattribute of the element may be compared to a known or dynamicallydetermined control rate of change (Step 260). In this way, system 100may be capable of making a recommendation about an element, a condition,and/or an event based on the rate of change of the attribute measured.For example, the recommendation may comprise a determination that anelement is susceptible to a condition based on a rate of changecorrelating with a control rate of change (or an associated rate of acontrol rate of change). The recommendation may comprise a determinationthat an element is resistant to a condition based on a rate of changenot correlating with a control rate of change (or an associated rage ofa control rate of change).

Microorganism Detection Based on Microorganism Information

In various aspects, computer-based system 100 may perform signaldetection and analysis of individuated elements of a sample (Step 305).In accordance with various embodiments and as described in detailherein, microorganism information or data from sample analyzer 130 maycomprise digital photomicrograph images, such as a set of dark fieldand/or fluorescent digital photomicrograph images obtained over a periodof time. In other embodiments, microorganism information from sampleanalyzer 130 may comprise non-image data. Although the functions thatmay be performed by analysis module 140 are generally described inrelation to image data in the present disclosure, the various functionsdescribed herein may be capable of parsing data from sample analyzer 130to identify local singularities (e.g., potential individuated organismsor discrete clones) regardless of whether the data comprises digitalimages having pixel intensity data, or comprises other forms of datawherein sample elements are measured and represented with quantitativesignal value and location information values (i.e., microorganisminformation values). The output for analysis module 140 following dataanalysis may comprise a matrix or table of discretely identified clonesand associated information obtained over the course of sample evaluation(i.e., values associated with attributes of the microorganism, ormicroorganism information value subsets). In various embodiments and asdescribed in greater detail herein, identification and tracking ofdiscrete clones and evaluation of attributes of those discrete clonesmay facilitate bacterial species identification, such as by fluorescencein situ hybridization, as well as assessment of clone growth, such asfor antibiotic susceptibility testing.

In various embodiments, analysis module 140 may perform steps includingseeding, registration, object model fitting, and clone tracking,outlined below and described in greater detail herein. Analysis modulemay perform these steps in any logical combination and sequence. Invarious embodiments, certain steps, for example, tracking, may not berequired due to features of the microorganism detection system used toperform sample evaluation. The various steps that may be performed byanalysis module 140 for image and non-image data are described brieflyin the following paragraphs and in greater detail below.

Seeding.

In various embodiments, seeding may be performed by analysis module 140as part of microorganism analysis. A seeding step may compriseperforming multiple functions including smoothing, denoising, andbackground estimation and segmentation. Image smoothing and denoisingmay be performed using techniques including spatial convolution withfilters. Background estimation and segmentation may be used to identifyregions of the images or other data that potentially correspond toclones (i.e. foreground information) and which regions contain purelybackground information. With these components distinguished, the seedingphase may also output a list of coordinates or addresses identified aspotential clones (i.e. seed points) for downstream analysis.

Registration.

Registration of seed points or sample objects may also be performed byanalysis module 140 in accordance with various embodiments. For example,following seeding, each image may be compared with the previous image ina time-lapse series to compute a step-wise registration shift atsubpixel resolution. This shift is essentially two numbers whichrepresent the translation (on the x and y axes) in pixels that isrequired for the images to be aligned. The registration process is alsocapable of detecting images which are too far out of alignment andshould not be considered for further analysis.

Microorganism Object Model Fitting.

In various embodiments, object model fitting may also be performed byanalysis module 140. The area or data around each seed point may beevaluated to form a list of models. These models describe a potentialcell, and may include, for example, various measurable or detectableattributes of a sample element such as signal intensity, size, shape,and orientation. Object model fitting may also evaluate which portionsof an image or non-image data are changing as time progresses. Thisobservation is critical in distinguishing cells from other measurementor data artifacts including, for example, dust particles.

Tracking.

In various embodiments, analysis module 140 may also track cells orclones throughout the acquired data in a dataset. For example, for imagedata, after cells have been identified for all of the images by objectmodel fitting, analysis module 140 may track the cells by performingcluster analysis of the cells to identify groups of cells that belong tothe same clone. This cluster analysis is computed via a distancemeasure, as new cells from clones will appear close to the parent cell.Once cells have been sorted into groups mapping to the correct clone,the increase (or decrease) in cell count, aggregate signal area, andaggregate signal intensity across each image or other data form iscomputed for each clone.

The various processes that may be performed by analysis module 140 aredescribed in greater detail in the following sections.

Signal Detection and Analysis Seeding

In various embodiments, detected signals and/or particles from data(e.g., an image) describing sample 110 (e.g., potential individuatedorganisms) are seeded or identified using a wavelet transform followingan initial cell candidate detection procedure, explained in more detailbelow. In various embodiments, signal detection and/or cell candidatedetection is applied to every image in a time-lapse stack. Morespecifically, analysis module 140 is capable of detecting signals,spots, and/or singularities in data that are greater than a threshold(Step 310). The threshold or background pixel signal evaluated in thecell candidate detection procedure may be comprised of imaging sensorartifacts, signal noise, and structure in the background such asillumination non-uniformities. The background structure and noisecharacteristics and data produced by the cell candidate detectionprocedure may be used in subsequent image processing steps such as modelfitting, described in detail in Example 11. Analysis module 140 maydetect signals having various properties including, for example, a rangeof intensities, a range of sizes, and a range of shapes. Each of thevarious properties may be associated with a predetermined threshold thatcharacterizes the level of a significant signal versus noise. In thisway, analysis module 140 may identify a signal as noise where a signalis not within a reference range (e.g., is not a significant signal). Forexample, in cases of image analysis for images with uneven backgroundillumination, analysis module 140 is able to determine whether a signalis a significant signal because the signal is evaluated relative to abackground signal-related threshold.

In various embodiments, analysis module 140 may employ a cell candidatedetection procedure to distinguish foreground pixels in an image frombackground pixels that may arise due to imaging sensor artifacts, signalnoise, and background structures or signal variations that may be due tonon-uniform illumination and the like. For example, image sensorartifacts can comprise bad pixels, including hot, warm, and cold pixelswherein the signal registered by the sensor does not correspond to anactual sample element or lack thereof in the imaged sample. Thenon-linear response of such pixels to incident photons may be detectedand suppressed or eliminated from further data processing in the cellcandidate detection procedure. In various embodiments, a cell candidatedetection procedure may suppress bad pixels using a local smoothingkernel. The local smoothing kernel may be convolved with input image Ito interpolate and suppress bad pixels in accordance with the followingfunctions:

${{S\left( {i,j} \right)} = {\sum\limits_{m = {- 2}}^{m = 2}\; {\sum\limits_{n = {- 2}}^{n = 2}\; {I\left( {{i + m},{j + n}} \right)}}}},{{where}\mspace{14mu} \left( {{m \neq 0},{n \neq 0}} \right)}$${S_{mean}\left( {i,j} \right)} = {\frac{1}{25}{\sum\limits_{{m = {- 2}},{n = {- 2}}}^{{m = 2},{n = 2}}\; {I\left( {{i + m},{j + n}} \right)}}}$${{S_{2}\left( {i,j} \right)} = {{\sum\limits_{m = {- 2}}^{m = 2}\; {\sum\limits_{n = {- 2}}^{n = 2}\; {I\left( {{i + m},{j + n}} \right)}^{2}}} - {\frac{1}{24}S^{2}}}},{{where}\mspace{14mu} \left( {{m \neq 0},{n \neq 0}} \right)}$${S_{2}\left( {i,j} \right)} = \left\{ {{\begin{matrix}{\sqrt{\frac{1}{23}{S_{2}\left( {i,j} \right)}},} & {{{if}\mspace{14mu} {S_{2}\left( {i,j} \right)}} \geq 0} \\{0,} & {otherwise}\end{matrix}{I^{\prime}\left( {i,j} \right)}} = \left\{ \begin{matrix}{\frac{1}{24}{S\left( {i,j} \right)}} & \begin{matrix}{{{if}\mspace{14mu} {S_{2}\left( {i,j} \right)}} > {0\mspace{14mu} {and}}} \\{\left( {{I\left( {i,j} \right)} - {S_{mean}\left( {i,j} \right)}} \right) > {10*{S_{2}\left( {i,j} \right)}}}\end{matrix} \\{{I\left( {i,j} \right)},} & {otherwise}\end{matrix} \right.} \right.$

Following interpolation of bad pixels, a second local smoothing kernelmay be applied to reduce the effects of shot noise, or statistical noiseresulting from photons arriving at an image sensor's photo-sensitivewells:

${S^{\prime}\left( {i,j} \right)} = {\frac{1}{9}{\sum\limits_{m = {- 1}}^{m = 1}\; {\sum\limits_{n = {- 1}}^{n = 1}\; {I^{\prime}\left( {{i + m},{j + n}} \right)}}}}$${I^{''}\left( {i,j} \right)} = \left\{ \begin{matrix}{{S^{\prime}\left( {i,j} \right)},} & {{{if}\mspace{14mu} {I^{\prime}\left( {i,j} \right)}} = {I\left( {i,j} \right)}} \\{0,} & {otherwise}\end{matrix} \right.$

In various non-optical microorganism detection systems, other sources ofstatistical noise and non-linear signal responses may similarly occurand be suppressed or eliminated.

Analysis module 140 may employ a wavelet transform for signal analysis.Analysis module 140 may employ the wavelet transformation and allow thesignal to decompose the signal into one or more wavelet functions. Thesefunctions can be applied to the image. In response to applying thewavelet functions to the image, the analysis module 140 may generate oneor more wavelet coefficients that represent an image on several levelsof resolution (e.g., a stack of image planes).

For example, analysis module 140 may employ one or more wavelettransforms by successively constructing approximations of one or moresignals (e.g., an image) at stepped or various resolution levels. Forexample, analysis module 140 may create a signal plane for a resolutionlevel of an image, having a general amplitude of approximately zero andthat has one or more points of amplitude (e.g., greater than or lessthan zero). These points of amplitude may correspond to particles (e.g.,microorganisms, noise, foreign objects, debris, and/or the like) thatcan be filtered, identified or otherwise analyzed. As a result, analysismodule 140 may reduce a signal (e.g., an image) to a sequence ofapproximations corresponding to the various resolution levels of thesignal. Moreover, analysis module 140 may determine a sequence of thewavelet coefficients (e.g., points of amplitude) that characterize thedetails of the image. These wavelet coefficients, when consideredtogether with the approximations at the various levels of resolution,describe the signal in a way that highlights the changes in the signal(e.g., the sample elements in the image), which may not be characterizedin detail in the various approximation of the signal.

In various embodiments, analysis module 140 may employ wavelettransforms because the wavelet transforms are suitable for signalcharacterization (e.g., object detection in images). By employingwavelet transforms, analysis module 140 produces a characterization of asignal that is relatively sparse. Put another way, because theapproximation of the signal plane has an amplitude that is near zero, apoint of signal amplitude may identify sample elements in a signalplane, and the wavelet transform is helpful for identifying individuatedmicroorganisms in a signal. This is so because the individuated organismcan be described by a point of amplitude in the plane (e.g., a deviationfor the near zero amplitude). Generally, this property of wavelettransforms may provide a representation where most of the waveletcoefficients are close to zero and only those that correspond tosignificant portions or features of the signal (e.g., an individuatedorganism or particle) are large. Moreover, the points of amplitudecorresponding to significant portions and/or features of the signalpersist through several resolution levels of the signal.

In various embodiments, by employing wavelet transforms, analysis module140 may not require significant computation resources. In this way,analysis module 140 may perform signal analysis with minimal and/orgenerally available computer resources, because the wavelettransformation analysis is computationally efficient.

In various embodiments, analysis module 140 may employ any suitablewavelet function and associated wavelet transform for signalapproximation. Each level k of the wavelet transform starts from anapproximation image A_(k−1) and produces two images. For example,analysis module 140 may determine a wavelet coefficient image W_(k) bysolving:

${{W_{k}\left( {i,j} \right)} = {{A_{k - 1}\left( {i,j} \right)} - {\frac{1}{4}{\sum\limits_{m,n}\; {A_{k - 1}\left( {{i + m},{j + n}} \right)}}}}},$

with high pass filtering, where A₀ is an original image, A_(k) and W_(k)for k=1, . . . , 8 are approximation and wavelet coefficient imagesrespectively on a resolution level k, indices (m, n)={(−k, 0), (0, −k),(k, 0), (0, k)} for levels k=1, 3, 5, 7 and (m, n)={(−k, −k), (k, −k),(k, k), (−k, k)} for levels k=2, 4, 6, 8, and i, jε{image areacoordinates}. In response to determining W_(k), analysis module 140 mayapproximate signal A_(k) by low-pass smoothing of A_(k−1), by solving:

${{A_{k}\left( {i,j} \right)} = {{A_{k - 1}\left( {i,j} \right)} + {\frac{1}{8}{\sum\limits_{m,n}\; {W_{k}\left( {{i + m},{j + n}} \right)}}}}},$

where A_(k) and W_(k) for k=1, . . . , 8 are approximation and waveletcoefficient images respectively on a resolution level k, indices (m,n)={(−k, 0), (0, −k), (k, 0), (0, k)} for levels k=1, 3, 5, 7 and (m,n)={(−k, −k), (k, −k), (k, k), (−k, k)} for levels k=2, 4, 6, 8, and i,jε{image area coordinates}.

In various embodiments, analysis module 140 may then reduce or eliminatenoise in the signal by filtering out small wavelet coefficients thatcorrespond to noise. Locations associated with points of amplitudewithin a predetermined threshold are retained as potential sampleelements (e.g., organism locations) and points of amplitude outside thepredetermined reference range are discarded as noise.

In various embodiments, analysis module 140 may determine an estimationof the wavelet coefficients that correspond to signal without noise oneach resolution level of the wavelet transform. This estimation may bebased on a Gaussian noise assumption and a non-informative priordistribution for the wavelet coefficient. The non-informative priordistribution may indicate that there is no initial assumption associatedwith data distribution. The wavelet coefficient estimation for oneresolution level may be calculated by solving:

${{W_{k}^{signal}\left( {i,j} \right)} = \frac{\left( {{W_{k}^{2}\left( {i,j} \right)} - \sigma_{k}^{2}} \right)_{+}}{W_{k}\left( {i,j} \right)}},$

where σ_(k) is a standard deviation of the wavelet coefficients thatcorrespond to noise on the resolution level k and W_(k) ^(signal) (i, j)is a wavelet coefficient at location (i, j) on a resolution level k of asignal without noise.

The wavelet coefficient estimation may use a value of σ_(k) for eachresolution level of the wavelet transform. In various embodiments,analysis module 140 may estimate σ, based on an assumption that most ofthe wavelet coefficients correspond to noise. This assumption may allowanalysis module 140 to determine noise wavelet coefficients using amedian of the absolute value of wavelet coefficients by solving:

${\sigma_{k} = \frac{{median}\left( {{W_{k}\left( {i,j} \right)}} \right)}{0.6745}},$

where the pixels (i, j) and corresponding W_(k) (i, j) values includedin the noise estimate are dependent on the resolution level k. Todetermine the resolution level k, analysis module 140 may decompose anoriginal image into various levels (e.g., 8) of the wavelet transform asdescribed above. This decomposition provides eight wavelet coefficientplanes of the same size as the original image. Analysis module 140 mayuse one or more high resolution planes or the signal to filter out thenoise. For each plane, analysis module 140 may deflate or normalize thewavelet coefficients by determining W_(k) ^(signal)(i, j). As a result,analysis module 140 may identify noise by determining which of thewavelet coefficients deflate or normalize to zero.

In various embodiments, analysis module 140 may also estimate σ_(k)values for each plane. For example, analysis module 140 may create anoise mask for each plane. For the first plane (e.g., the highestresolution plane) the mask may include all wavelet coefficients. For thesecond, third, and fourth planes, the mask may include only thelocations that are detected as noise (e.g., wavelet coefficientsdeflated or normalized to zero) in the previous plane. Analysis module140 estimates the noise standard deviation for each plane based on thedetermination of σ_(k) by using the wavelet coefficients that arecovered by the noise mask.

In various embodiments, analysis module 140 may analyze the lowerresolution planes (e.g., the fifth to eighth planes) to detectsignificant wavelet coefficients (e.g., sample elements or signalfeatures that are not noise and that may correspond to organisms).Analysis module 140 may employ the same wavelet coefficient deflationprocedure, namely the estimation of σ_(k). However, analysis module 140may employ a control based on a predetermined or dynamically determinedrule. The rule may provide that the wavelet coefficients are set to zeroin response to the wavelet coefficients being deflated or normalized tozero on both planes from paired planes (e.g., the fifth and sixth planesand/or the seventh and eighth planes in an eight plane analysis). Inresponse to the normalization or deflation not being zero, the deflatedwavelet coefficients can be set to one. As a result, each locationcorresponding to a one is not discarded altogether. Rather, the locationis monitored as a potential seed site (e.g., a site where an organism ispresent), as described in more detail below. σ_(k) values are estimatedusing all wavelet coefficients of the plane, there is no mask. This maybe a two-step filtering process. For example, the first filtering stepmay detect noise and the second filtering step may detect particles. Amask may be used to define areas of an image that contain only noisewavelet coefficients.

In various embodiments, analysis module 140 may calculate waveletcoefficient correlation planes by taking products of the deflatedwavelet coefficients at the same location on all of the various waveletcoefficient planes. This correlation may allow analysis module 140 toidentify one or more correlation points across the various planes. Inthis way, analysis module 140 is able to determine potentialmicroorganism locations, of variable size and morphology, in thepresence of noise and uneven background illumination with a manageablefrequency of false positive events while minimizing the frequency offalse negative events.

In various embodiments, analysis module 140 may employ a discretewavelet transform to construct a per-pixel estimate of significantforeground presence, and thereby identify seed locations for candidatecells. The discrete wavelet transform may also decompose each image intohigh- and low-frequency components at multiple levels of resolution. Adiscrete wavelet transform may be used to decompose each image into anapproximation image A and a coefficients image C at every level ofresolution k, for example, k=1, . . . , 8. Thus, for every level ofresolution k, the discrete wavelet transform comprises two convolutions,including a high-pass filter of the previous approximation image A_(k−1)to produce a coefficients image C_(k) by solving:

C _(k)(i,j)=A _(k−1)(i,j)−¼Σ_(m,n) A _(k−1)(i+m,j+n),

and a low-pass filter of A_(k−1)to produce an updated approximationimage A_(k) by solving:

${{A_{k}\left( {i,j} \right)} = {{A_{k - 1}\left( {i,j} \right)} + {\frac{1}{8}\Sigma_{m,n}{C_{k}\left( {{i + m},{j + n}} \right)}}}},{{{where}\left( {m,n} \right)} = \left\{ \begin{matrix}\left\{ {\left( {{- k},0} \right),\left( {0,{- k}} \right),\left( {k,0} \right),\left( {0,k} \right)} \right\} & {{if}\mspace{14mu} k\mspace{14mu} {is}\mspace{14mu} {even}} \\\left\{ {\left( {{- k},{- k}} \right),\left( {k,{- k}} \right),\left( {k,k} \right),\left( {{- k},k} \right)} \right\} & {{if}\mspace{14mu} k\mspace{14mu} {is}\mspace{14mu} {{odd}.}}\end{matrix} \right.}$

The presence of significant coefficients is then determined fromanalyzing coefficient images C_(k) in accordance with the followingprocess. First, resultant wavelet coefficients are interpreted toconstruct a noise presence mask using the first four coefficient imagesk=1, . . . , 4 by solving:

  C_(k)^(noise)(i, j) = (C_(k)(i, j)² − 3 * σ_(k))/C_(k)${W_{noise}\left( {i,j} \right)} = \left\{ \begin{matrix}{{\prod\limits_{{k = 1},\ldots \mspace{14mu},4}\; {C_{k}^{noise}\left( {i,j} \right)}},} & {{{{{if}\mspace{14mu} {C_{k}^{noise}\left( {i,j} \right)}} > {0\mspace{14mu} {for}\mspace{14mu} {all}\mspace{14mu} k}} = 1},{\ldots \mspace{20mu} 4}} \\{0,} & {otherwise}\end{matrix} \right.$

where σ_(k)=median(|C_(k)(i, j)|/0.6745 for i,j such that W_(noise)(i,j)=0, is an estimate of noise at a given resolution.

Next, two pairs of wavelet coefficient images, k=5,6 and k=7,8, are usedto estimate contributions due to cell candidates. For k=5,6:

  C_(k)^(signal)(i, j) = (C_(k)(i, j)² − 3 * σ_(k))/C_(k)${W_{signal}^{1}\left( {i,j} \right)} = \left\{ {\begin{matrix}{0,} & {{{{{if}\mspace{14mu} {C_{k}^{signal}\left( {i,j} \right)}} \leq {0\mspace{14mu} {for}\mspace{14mu} {all}\mspace{14mu} k}} = 5},6} \\{{\prod\limits_{{k = 5},6}\; {C_{k}^{signal}\left( {i,j} \right)}},} & {otherwise}\end{matrix},} \right.$

where σ_(k)=median(|C_(k)(i, j|)/0.6745 for all i,j. W_(signal) ² (i,j)is computed for k=7, 8 using an analogous function.

The final wavelet coefficient image is a product of noise and two signalcoefficient images, which results in a non-zero coefficient determinedfor significant (i.e., lower frequency) foreground events only:

P(i,j)=W _(noise)(i,j)W _(signal) ¹(i,j)W _(signal) ²(i,j).

Candidate cell locations, or seed locations, are then computed by alocal maxima operator:

Seed(n)=argmax_([i±m,j+n])(P(i+m,j+n)),

where (m, n)ε({−2,2}, {−2,2}).

Microorganism Object Model Fitting

In various embodiments, analysis module 140 may assign uniqueidentifiers (e.g., locations) to signals (Step 315). By monitoring thelocations, analysis module 140 may estimate morphological parameters ofthe identified signal objects (e.g., detected potential organisms).

In various embodiments, analysis module 140 may determine potentiallocations based on the particle seeding from a signal. Analysis module140 may further evaluate the identified locations to determine which ofthe identified locations correspond to the actual microorganisms. Assuch, analysis module 140 may model each identified location (Step 320).For example, analysis module 140 may estimate particular attributes of aparticular sample element. The particular attributes may be associatedwith a microorganism location, such that microorganisms attributes,activities and/or the like can be further evaluated, characterized, orestimated by system 100. More specifically, analysis module 140 maycreate a microorganism object characterized by length and width insignal parameters (e.g., pixels in the context of an image, impedanceregistering electrodes in a microelectrode array, and the like),orientation angle in the plane on a signal (e.g., in radians), andheight in signal intensity units of an image for each identifiedlocation. Additionally, analysis module 140 may determine the signalintensity of an object associated with the identified location (e.g.,microorganism signal intensity may be calculated as a microorganismobject parameter). In various embodiments, the signal intensity may be acomposite value representing a plurality of measured attributes,including, for example, a plurality of pixel intensities associated witha sample element.

Analysis module 140 may estimate, determine, and/or characterize thedimensional parameters of objects at identified sites by fittingmicroorganism models to data associated with each location. The modelmay be characterized as a second-order surface such as, for example:

M _(ij)(x,y)=a ² x ² +b ² y ² +cxy+d,

illustrated in FIG. 27A.

Analysis module 140 may fit the surface associated with the location byminimizing the squared error to an original image at the location (i,j).The coefficients of the surface a, b, c and d are used to determine theparameters of the object associated with the location. For example,analysis module may calculate the length, width, and orientation angleof the object using the eigenvalue decomposition of the matrix

$\begin{bmatrix}{2a} & c \\c & {2b}\end{bmatrix}.$

Microorganisms may look like brighter circular or rod-like spots on adarker background. Based on this determination, analysis module 140 maydiscard certain identified locations. For example, only objects and/orassociated object locations with both positive eigenvalues or a largerpositive eigenvalue may be retained as potential microorganisms. Theheight of the object associated with the location may be assigned thevalue of d.

Using the parameters determined for each object model at each identifiedlocation, analysis module 140 may create a microorganism object as arectangular box with a length and width equal to the correspondingmicroorganism object's length and width and the height of 1. Analysismodule 140 may smooth the box by convolving with a Gaussian kernel ofwidth 7. The resulting object height may be scaled up to the height ofthe microorganism object parameter d. This smoothed box may be used tocalculate an error of the microorganism object as an absolute deviationbetween the smoothed box and the original image at the correspondinglocation. The error may be used as a threshold range or limit toidentify microorganism object sites (e.g., organisms for evaluation) andeliminate debris and/or foreign objects that are not microorganismobjects. For example, analysis module 140 may identify microorganismobjects with errors of less than 300 as detected microorganisms. Inresponse to meeting the threshold range, analysis module 140 mayrepresent each selected microorganism as a microorganism object withcorresponding parameters.

In various embodiments, analysis module 140 may evaluate detected imageobjects by employing model fitting in accordance with a process setforth in greater detail in Example 11, below. In various embodiments,model fitting may rely on products of other processes performed byanalysis module 140, such as seeding (described above) and registration(described below).

In various embodiments, analysis module 140 may determine and/orcalculate a microorganism composite value parameter (e.g., a compositevalue representing a plurality of measured or estimated attributes). Themicroorganism composite value parameter may be determined based on thesum of measured object values minus background values. For example, acomposite value may comprise the signal intensity of pixels in theoriginal image covered by the microorganism object box minus imagebackground for these pixels. If microorganism objects overlap, analysismodule 140 may distribute the signal intensity value between overlappingmicroorganism object locations in proportion to the values of themicroorganism object boxes over the corresponding pixel.

In various embodiments, analysis module 140 may create a backgroundsignal based on the first signal of the signal sequence (with the leastnumber of microorganisms). The first signal may be transformed bydeleting all areas that belong to any microorganism object (regardlessof whether the microorganism object is retained as a microorganism ordiscarded because of a large fitting error). In response to thetransformation, analysis module 140 may substitute the pixel values inthese areas with the median of the lower 50th percentile of 800 closestpixels that do not belong to any other identified location andcorresponding microorganism object. Analysis module 140 also smoothesthe background image by a median filter of size 10 in order to eliminatenoise. The smoothed background image may be saved and used as thebackground for all images in the sequence.

As such, analysis module 140 is able to estimate and/or characterize thelength, width, and/or height of microorganism objects based on imagelocal curvatures at identified locations. Moreover, analysis module 140may reduce associated errors by eliminating identified locations andassociated seed objects in response to the fit quality of microorganismobject being outside a predetermined reference range with respect to theoriginal image.

Clone Tracking

In accordance with various embodiments, analysis module 140 may track anindividuated microorganism. Clone tracking may not be required invarious embodiments, such as methods of microorganism handling and dataacquisition that do not rely on data processing steps for distinguishingand identifying individual microorganisms. For example, and as describedin greater detail below in Example 13, various methods may be usedwhereby individual cells or clones are physically isolated or containedat a discrete location with no risk of relocation, overlapping growth,or the like. However, in accordance with various embodiments,microorganisms may be able to move or relocate over the course of dataacquisition, and clone tracking may be required to ensure that data isacquired for the same individual microorganism over time and therebycontribute to data integrity.

In various embodiments, analysis module 140 may track each microorganismfrom a first image through the image sequence individually. Analysismodule 140 may assign microorganisms in the first image to clones (e.g.,divided or new microorganisms at a monitored object location). Moreover,analysis module 140 may create clone tracks as combinations ofindividual microorganism tracks from the same clone. This allowsanalysis module 140 to evaluate which microorganisms belong to whichclone for the first image of the sequence, where the number ofmicroorganisms is the smallest and clones may be well separated. Bycreating an assignment or track for each clone, analysis module 140 maydetermine a baseline associated with microorganisms and clones from thefirst signal. Analysis module 140 may then evaluate the clone progressand/or behavior automatically using the microorganism track assignments.

In various embodiments for which clone tracking is implemented, analysismodule 140 may employ one or more rules for determining trackassignments. For example, a track may be a sequence of sets ofmicroorganisms assigned to a particular track in each signal of a signalsequence. The track may start as one microorganism, and eachmicroorganism of the track can be associated with zero, one, or severalmicroorganisms in the next signal of the sequence. Each microorganism inthe first signal of the signal sequence can be a start of amicroorganism track. In various embodiments, the start or origin of eachmicroorganism track corresponds to the first signal of the sequence,with no new tracks created thereafter. Tracks can end before the end ofa signal sequence. Microorganisms in a subsequent signal of the sequenceshould have predecessors in the previous signal. Microorganisms that aretagged as non-growing can be assigned as a predecessors to no more thanone microorganism in the next signal of a sequence. The microorganism inthe next signal should represent the same microorganism as itspredecessor with high probability. Tracks may be created by associatingmicroorganisms in a subsequent signal of the signal sequence with theclosest microorganisms in the previous signal. This may be done byevaluating a change in a characteristic or event (e.g., a location)associated with a subject cell and an associated microorganism.

Registration

In various embodiments, the first signal sequence can be registered toeliminate alignment shifts from one signal to the next. Signalregistration can be performed on signal sequences regardless of whethercorrelating elements between the signals are present in the signals.Correlating elements may be used as an alignment marker (e.g., fiducialmarks in an image). Otherwise, the detected microorganisms may be usedas alignment markers. Registration may be determined by finding theminimal translation of a signal that minimizes the mean squared error ofits alignment markers with the previous signal in the sequence.

In accordance with various embodiments, registration may be performedemploying a process described in greater detail in Example 12, below.

In response to registration, non-growing microorganism objects may bedetected. For the first five cycles of the signal sequence eachmicroorganism may be matched to the closest microorganism in the nextsignal in the sequence. As a result, analysis module 140 may determine alist of single-microorganism tracks through the first five cycles of thesignal sequence. Analysis module 140 may calculate two measures ofvolume difference for all possible pairs of microorganisms in a track.The first measure may be the size of the non-overlapping volume of themicroorganism models that are placed at the same location but retaintheir orientation. The second measure may be the difference in volumebetween the microorganism models regardless of their orientations. Forthe five cycle tracks there can be ten pairs of microorganisms thatproduce two vectors of difference measures of length ten. Amicroorganism in the first signal of the sequence can tagged by analysismodule 140 as non-growing if the square root of the mean of the squaresis less than 0.3 for the first vector of difference measures and is lessthan 0.5 for the second vector.

The implementation of the distance measure calculation for allmicroorganisms in a first image to all microorganisms in a second matrixmay be computationally expensive, especially in cases of hundreds orthousands of microorganisms. Therefore, the distance measure may becalculated iteratively in order make the computational expense of thiscomponent of the tracking process feasible.

More specifically, analysis module 140 may place each microorganism fromthe first signal in the same signal with all microorganisms of thesecond signal. For each such microorganism configuration an adjacencymatrix can be calculated with a Gaussian similarity function:

${s\left( {i,j} \right)} = {\exp {\frac{{X_{i} - X_{j}}}{2\sigma_{dist}^{2}}.}}$

Analysis module 140 may determine that this adjacency matrix can beinterpreted as a transition probability matrix of a random walk thatjumps randomly between microorganisms. The distance parameter σ_(dist) ²of the similarity function may be initially set to 60. The distanceparameter may be set to this level to make the similarity functiongreater than zero for most microorganisms that are ancestors of the samemicroorganism.

Each microorganism in the first signal may be assigned a vector oflikelihoods of being a predecessor for all microorganisms in the secondsignal based on the distance parameter. The Laplacian of the adjacencymatrix s(i,j) may be constructed. Analysis module 140 may obtaineigenvectors that correspond to the smallest eigenvalues (<0.0005) ofthe Laplacian. These eigenvectors may approximate indicator functions ofmicroorganism clusters in the microorganism configuration initiallyconstructed. The eigenvectors may be combined as columns into the matrixU, and the matrix Q=UU^(T) may be constructed. A row of a matrix Q thatcorresponds to a microorganism from the first signal is taken as alikelihood vector of this microorganism being the predecessor for themicroorganisms on the second signal.

In various embodiments, as likelihood vectors for all microorganisms inthe first signal being are collected, analysis module may associate eachmicroorganism in the first signal with zero, one, or severalmicroorganisms in the second signal. For each microorganism in thesecond signal, analysis module 140 evaluate whether any identifiedmicroorganism object may be a predecessor microorganism in the firstsignal, based on the highest likelihood value. Based on the highestlikelihood value, analysis module 140 may make an association betweenmicroorganisms if the likelihood value is the highest for all firstsignal microorganisms.

In various embodiments, analysis module 140 may identify and separatelyanalyze non-growing microorganisms. For example, analysis module 140 mayassociate non-growing microorganisms with zero, or one microorganism inthe second signal, and the microorganism in the second signal shouldhave similar signal characteristics to the microorganism in the firstsignal. For example, analysis module may use a similarity threshold offive-fold for both volume difference measures calculated duringnon-growing microorganism detection. Analysis module 140 may tagmicroorganisms in a subsequent signal that have been associated withnon-growing microorganisms in a previous signal as non-growingmicroorganisms to propagate non-growing microorganisms through thesignal sequence.

All microorganisms that are assigned a track association are deletedfrom the second image and the association step is repeated until thereare no possible associations left between two images. In this way,microorganisms identified in the second image are associated with and/orassigned to a clone in the first image.

In various embodiments, if there are non-associated microorganisms leftin subsequent signal analysis, module 140 initiates a trackingprocedure. A new microorganism configuration associated with thesubsequent signal for each microorganism of the previous signal may becreated by removing all previously associated microorganisms from thesubsequent signal except the ones that have been associated with theparticular previous signal. As a result, analysis module 140 mayincrease σ_(dist) ² and the non-associated microorganisms and repeattracking through the signal sequence.

Analysis module 140 may associate and/or cluster microorganisms in thefirst signal of the signal sequence into clones. Analysis module 140 mayassociate the clusters based on the clones having the substantiallysimilar spectral clustering distances which were identified duringtracking. As such, analysis module 140 may create clone tracks that arecombinations of microorganism tracks that start from the first imagemicroorganisms of the same clone.

Growing Clone Detection

In various embodiments, analysis module 140 may detect potential growingclones based on a probability analysis. For example, analysis module 140may assign a probability of being a growing clone or a valuecorresponding to a likelihood of being a growing clone to each trackedclone. Likewise, analysis module 140 may also assign a probability ofbeing a growing clone to any clone for which an attribute may bemeasured over time, regardless of whether a clone tracking function isperformed by analysis module 140. The probability value can be anysuitable value. For example, the likelihood may be a value between zeroand one, where one represents the highest likelihood. Analysis module140 may also be configured to determine a growth likelihood value foreach tracked clone. In various embodiments, a growth likelihood may becalculated based on pixel intensity data derived from an optical image.For example, the growth likelihood value may be calculated as a productof two likelihood values (e.g., the clone signal intensity curve shapelikelihood and the tracking error likelihood). In various embodiments, agrowth likelihood may be calculated based on non-optical data, such asmass data, impedance data, analytical chemistry data, and the like. Invarious embodiments, a growth likelihood may be calculated based on anyattribute or combination of attributes that may be measured using anysuitable method.

In various embodiments, analysis module 140 may further clarify, refine,and/or eliminate some tracked clones. For example, clones that are nottracked to the end of signal sequence may be assigned a growthlikelihood value of zero.

In various embodiments, analysis module 140 may further characterize oneor more tracked close based on signal intensity curves of trackedclones. The signal intensity curve for a clone may be quantified as thesequence of sums of intensities of a population (e.g., each individuatedmicroorganism) assigned to the particular clone, which is evaluated ortracked in each signal of the signal sequence. For example, a cubicpolynomial p2^(x2)+p3^(x)+p4 may be fitted to or approximate the naturallogarithm of the signal intensity curve, where parameters p1, p2, andp3^(x) and the mean squared error of the fit characterize features ofthe signal intensity curve. These features may serve as input for twologistics regression functions that in turn output the two likelihoodvalues (e.g., clone signal intensity curve shape likelihood and thetracking error likelihood).

In various embodiments, analysis module 140 may determine the trackingerror likelihood. The tracking error likelihood may be determined basedon a logistic regression function that takes the mean squared error ofthe fit of the signal intensity curve as an input. This likelihood canrepresent an assumption that the logarithm of the signal intensity curveof a growing clone should have a signal intensity curve that is wellapproximated by a cubic polynomial.

In various embodiments, analysis module 140 may determine the clonesignal intensity curve shape likelihood logistic regression function andtake the cubic polynomial parameters p1, p2, and p3 as input. Based onthis input, analysis module 140 may determine that the logarithm of thesignal intensity curve of a growing clone should have a specific shape.

In various embodiments, analysis module 140 may determine any suitablevariant response function. Similarly, analysis module 140 may be capableof characterizing a response based on any mathematical function and/orestimating the mathematical function associated with a response.

In various embodiments, the logistic regression functions have beenconstructed by fitting logistic regression models to a training set oftracked clones. The training set included examples of tracked growingand non-growing clones from several evaluations such as those describedherein.

In various embodiments, analysis module 140 may fit a cubic polynomialto the logarithm of the signal intensity curve on a clone-by-clone basisfor the signal data sequence. Analysis module 140 may use a logisticregression function in the cubic polynomial parameter space to assign agrowth likelihood value for a clone.

In various embodiments, growth likelihood information and other datathat may be derived from analysis of growth by analysis module 140 maybe used to perform antibiotic susceptibility data analysis. Antibioticsusceptibility data analysis uses measurements that reflect the changingmass of clones over time to detect bacterial susceptibility toantibiotics. The growth data may be represented by a growth profile foreach clone.

In various embodiments, other growth parameters may be determined fromsignal intensity curves or growth curves derived for a population ofgrowing clones using various forms of data other than optical imagedata. Growth parameters may include, for example, the number of growingclones, a ratio of sample number of growing clones to a control numberof growing clones, a sample clone division rate, a ratio of a sampleclone division rate to a control clone division rate, growthprobability, a growth time score, a sample fast clones growthprobability, a ratio of a sample fast clones growth probability to acontrol fast clones growth probability, a division rate for sample fastclones, and the like.

Regression analysis of various growth parameters for strains with knownMICs may be used to produce a concentration susceptibility model for abacterial species and an antibiotic combination. This model, whenapplied to growth parameters calculated for an unknown bacteria sample,may be used to output a continuous score. The score may be used todescribe antibiotic susceptibility of the bacteria sample on acontinuous scale. The susceptibility score may be thresholded at knownbreakpoints to output an estimated MIC value.

EXAMPLES

In various embodiments, the examples described herein can demonstrateevaluation of various aspects of microorganism attributes, as definedherein. The examples described herein can also demonstrate determinationand/or characterization of changes in attributes (e.g., growth rate), inresponse to, for example, events or conditions. Moreover, the examplesdescribed herein may demonstrate analysis of the changes in attributesto make a determination of alternation of growth rates as compared toreference growth rates. Based on these determinations, the systems,methods and computer readable mediums (e.g., system 100), may performthe evaluation steps and provide one or more outputs based on thedetermined alterations.

Example 1 Identification and Growth Rate Quantitation of IndividualBacterial Clones Using a Microfluidic Concentration Device

The ability of the system of the present disclosure to provideimmunochemical and microscopic identification and quantitative growthrate measurement by evaluating in near real time and in situ the massincrease of individual microorganisms was assessed. The system providesa rapid, accurate evaluation of growth dynamics for the population ofviable organisms in the sample.

Bacterial species display unique surface antigens enabling specificimmunolabeling and identification with a multiplexed automated digitalmicroscopy (MADM) system. Proof of concept was demonstrated byconcentrating a sample of mixed Klebsiella pneumoniae and Haemophilusinfluenzae to a surface and incubating with a mixture of anti-Kp andanti-Hi. Subsequent species-specific secondary fluorescent labelingidentified bacterial species via fluorescent imaging, results of whichare shown in FIG. 6.

Growth rates were determined in accordance with various processesdisclosed herein to identify microorganisms via thresholddiscrimination, and track growth by tabulating integrated intensitythrough a timed sequence of dark field images. Growth constants anddoubling times of individual clones are derived as explained herein, andcan be expressed as either individualized or aggregate subpopulationvalues.

Standard methods of microorganism growth rate quantitation measureoptical density (OD₆₀₀) changes in a growing suspension culture. Theexperimental system was used to determine the aggregate growth rates ofa panel of ten clinically relevant bacterial species, with the resultscompared to suspension culture measurements. The average difference of18% sd 5% demonstrates a high degree of concordance in the methods.

Individual bacterial clone growth tracking enables evaluation ofantibiotic susceptibility and resistance characteristics on a clonalbasis within hours with near real time measurement.

TABLE 1 Exponential growth curve constants and doubling times of 6tracked clones of A. baumannii. Average value derived from means ofindividual time points. Clone ID k (min⁻¹) R² DT (min) 45 0.0169 0.91740.9 63 0.0174 0.973 39.9 83 0.0175 0.962 39.5 88 0.0175 0.967 39.5 1060.024 0.983 28.8 113 0.0215 0.972 32.3 Average 0.018 0.986 38.6

FIGS. 5A and 5B illustrate electrokinetic concentration (EKC) of S.aureus. Phase microscopy image at time=0 (FIG. 5A) shows out-of-focusmicroorganisms in bulk solution beyond the image plane (surface). Theimage field of view is 202×187 p.m. After 50 seconds of EKC,microorganisms have been driven to the surface and concentration iscomplete (FIG. 5B). During EKC, phase images are collected at a rate ofapproximately one per second, and image analysis in accordance with thepresent disclosure is used to rapidly count particles (microorganisms).FIG. 5C shows EKC curves for a panel of 8 clinically relevant bacterialspecies.

FIGS. 6A-6D illustrate immunolabeling of selected bacterial strains inaccordance with various embodiments. A mixed sample of Klebsiellapneumoniae ATCC 49472 and Haemophilus influenzae ATCC 10211 wasconcentrated in a flowcell. A mixture of anti-K. pneumoniae and anti-H.influenzae was incubated in the cell, followed by fluorescently labeledsecondary antibodies. Image acquisition with fluorescent microscopydemonstrates bacterial species labeling. FIG. 6A illustrates mixedbacteria concentrated to the surface. The circle to the left indicatesK. pneumonia and the circle to the right indicates H. influenza. FIG. 6Billustrates K. pneumonia labeled with mouse anti-K. pneumonia andanti-mouse Alexa Fluor® 546 (goat anti-mouse IgG, Life Technologies,NY). FIG. 6C illustrates H. influenza labeled with rabbit anti H.influenza and anti-rabbit Alexa Fluor® 647 (goat anti-rabbit IgG, LifeTechnologies). Circle indicates H. influenza. FIG. 6D illustrates K.pneumonia (left circle) and H. influenza (right circle).

FIG. 7 illustrates a series of darkfield images of Acinetobacterbaumannii showing growth over 90 minute period and microorganismanalysis in accordance with various embodiments (FIG. 7A). Integratedintensity curves of a dataset of 6 clones identified and evaluated usingthe system described herein are shown (FIG. 7B). A growth curve derivedfrom the average integrated intensity values from each time point isalso illustrated (FIG. 7C).

FIG. 8 illustrates doubling times in minutes of ten bacterial strains,measured with the system of the present disclosure and by OD₆₀₀ changein broth culture suspension. Each value is the average of minimum threeruns, with each run consisting of four replicates.

FIG. 9 illustrates growth and kill curves of a set of experiments inwhich Klebsiella pneumoniae was concentrated, grown, and dosed withvarying concentrations of imipenem, a lytic antibiotic (FIG. 9A).Tracking the decrease in integrated intensity after introduction ofantibiotic at time=90 min reveals dose dependent kill rates. FIG. 9Bshows 50% kill time as a function of antibiotic concentration.

Example 2 Same-Day Blood Culture with Digital Microscopy Introduction

Bacteremia due to multiple drug resistant organisms (MDRO) is increasingin frequency and growing in complexity. Critically ill patients whoacquire a bloodstream infection must begin adequate antibiotic therapyas quickly as possible. For critically ill patients, resistance canrender initial therapy ineffective, delaying the start of effectiveantimicrobial therapy. The requirement for overnight culture creates anunacceptable delay. Delay also prolongs exposure to broad-spectrumempiric therapy, creating selective pressure favoring emergentresistance. Systems and methods in accordance with various embodimentsof the present disclosure, such as the multiplexed automated digitalmicroscopy (MADM) system referred to with respect to the followingexamples, have the potential to reduce turnaround time by rapidlyanalyzing live bacteria extracted directly from a clinical specimen,eliminating the need for colony isolates. The purpose of this pilotstudy was to determine MADM system sensitivity, specificity, speed, andtechnical requirements for same-day analysis of live organisms extracteddirectly from blood. Tests used two of the most common ICU pathogens,Staphylococcus aureus (SA) and Pseudomonas aeruginosa (PA).

The MADM system used a custom microscope and pipetting robot, plus anevaluation system, as described herein. 32-channel disposable cassettes(FIG. 4A) enabled live microorganism immobilization for microscopy andfluid exchanges for different test agents. The microscope scanned 40image fields in each flowcell channel, each channel having organismsextracted from about 3.5 μL of prepared inoculum.

Simulated blood specimens consisted of isolates spiked into 10 mL eachof 29 aliquots of two short-fill CPD blood bank bags to makeapproximately 5 CFU/mL of bacterial target species, confirmed byquantitative culture. Spiked isolates included 14 Staphylococcus aureus(SA), 3 Pseudomonas aeruginosa (PA), or 12 non-target Gram-negativebacilli species. Dilution of each sample with 30 mL of modified TSBculture medium promoted growth. 20 additional control aliquots containedno spikes. 4-hour incubation at 35° C., followed with brief spincleanup, ended with pellet resuspension into an electrokinetic buffer tomake 1 mL samples for MADM system analysis. 20 μL sample aliquots werethen pipetted into 14 cassette flowcells. A 5-minute low-voltageelectrokinetic capture was performed to concentrate microorganisms onthe lower surface of each flowcell where a capture coating immobilizedthe bacterial cells.

Liquid (40° C.) Mueller-Hinton agar with and without antimicrobials wasthen exchanged through each channel and gelled. Separate pairs ofchannels received antibiotics, which included the following antibioticsat the concentrations indicated: 32 μg/mL amikacin (AMK), 8 μg/mLimipenem (IPM), 6 μg/mL cefoxitin (FOX), or 0.5 μg/mL clindamycin (CLI).Cooling then gelled the agar, followed by incubation at 35° C. withmicroscope imaging at 10-minute intervals for 3 hours (concurrent withidentification in other channels).

The system acquired dark-field images every 10 minutes. The analyzerapplied identification algorithms to each individual immobilized cellthat exhibited growth. 6 channels provided data for ID algorithms toscore individual organisms and their progeny clones. ID variablesincluded cell morphology, clone growth morphology, clone growth rate,and other factors. The analyzer computed ID probability based on thenumber of related clones and their scores. The system required 40 ormore clones that exceeded a threshold score in order to proceed withanalysis.

Controls included quantitative culturing, disk diffusion tests forisolate resistance phenotype, and 20 blood samples without spikes.

Results

Culture confirmed that normal growth occurred in the prepared samples.Organism detection required ≧4 growing clones (GC). Recovery yielded SAGC counts that exceeded CFU as determined by culturing because ofnear-complete clump disruption in most samples. Counting combinedresults in multiple channels when appropriate. Identification required≧40 GC, and each phenotype test required ≧40 GC. The MADM systemdetected growth in 29/29 spiked samples and no growth in 20/20non-spiked controls. Growth sufficient for ID occurred in 23/29 samplesin the fixed 4-hour growth period. 4 SA samples clumped excessively,precluding ID scoring. 2 PA samples grew too slowly (<1.1 div/hr) toachieve 40 GC in the growth period (5 hours would suffice). SA growthrates were ≧1.5 div/hr. The MADM system identified 1/1 PA and 10/10 SA.One false PA ID occurred out of 22 non-target samples to yield 100%sensitivity and 97% specificity. The false ID was attributable to aknown imaging abernation, later corrected. FIGS. 10A-10C illustrateexamples of dark-field images acquired using a system in accordance withvarious embodiments of the present disclosure over a period of 3 hours(time points at 0, 60, 120 and 180 minutes) of clone growth for SAwithout drug (FIG. 10A, no antibiotic), SA in 6 μg/mL FOX (FIG. 10B,cefoxitin) growth indicating MRSA phenotype, and for a Gram-negative rod(E. coli) without drug (FIG. 10C) for morphology comparison. Brighterareas represent 3-dimensional growth effect (more light scattering fromlayering or end-on rod orientation). Non-growing particles are assumedto be debris.

The MADM system was used to identify drug resistance in 19/20 adequatesamples with one false MSSA, yielding drug resistance phenotypingresults with 89% sensitivity and 100% specificity. Table 2 summarizes SAdata for overall concordance with comparator results.

Discussion

This pilot study asked whether major pathogens grow quickly enough toenable same-day diagnostic testing directly with bacteremic bloodsamples using microscopy. The MADM system had previously been used toanalyze small numbers of live microbial cells extracted from otherspecimen types.

This study demonstrated that 4 hours of growth in a common nutrientmedium provides enough live clones for MADM system analysis withfast-growing cells (>1.1 div/hour growth rate in the conditions tested).PA required slightly longer times for adequate testing, estimated at 5hours. Given the number of GC required for a test (40 with the studyprototype), number of tests, and the slowest target organism growth,straightforward calculation derives the minimum growth duration needed.Fastest possible turnaround time results from maximizing growth ratewhile minimizing the GC needed per test, and minimizing the requirednumber of tests and their duration.

TABLE 2 Identification of S. aureus drug resistance phenotypes usingMADM system analysis. S. aureus True Neg True Pos AccuracyIDENTIFICATION (Adequate Growth N = 23) MADM-Pos 0 10 Sens 100% (CI66-100%) MADM-Neg 23 0 Spec 100% (CI 72-100%) PHENOTYPE: MRSA (AdequateGrowth N = 10) MADM-Pos 0 4 Sens 80% (CI 30-100%) MADM-Neg 5 1 Spec 100%(CI 46-100%) PHENOTYPE: CLI-R (Adequate Growth N = 10) MADM-Pos 0 4 Sens100% (CI 40-100%) MADM-Neg 6 0 Spec 100% (CI 52-100%)

Within 8 hours starting with blood, automated microscopy successfullyidentified target pathogens and detected drug resistance phenotypes fora major species of live bacterial cells extracted directly from a smallvolume of simulated bacteremic blood. Diagnostic analysis usingindividual live-cell methods enables rapid turnaround without firstrequiring colony isolates. The probabilistic identification scoringachieved high concordance with clinical lab results. Resistancephenotype analysis also achieved high concordance. This analyticalstrategy can also use responses of individual clones to identifyorganism subpopulations and resistance phenotypes within polymicrobialspecimens.

Conclusion

Application of systems and methods in accordance with variousembodiments of the present disclosures, such as MADM system analysis,enables diagnostic analysis of live microorganisms extracted after briefgrowth in culture medium with high specificity and sensitivity.

Example 3 3-Hour ESBL Detection from Positive Blood Cultures Using aMultiplexed Automated Digital Microscopy (MADM) System Introduction

Infections due to Gram negative bacteria expressing extended-spectrumbeta-lactamases (ESBLs) are increasing in frequency and growing incomplexity. ESBL drug resistance expression can be difficult toaccurately detect by standard culturing methods, and is associated withmultiple drug resistance in nosocomial and community infections. Forcritically ill patients, the likelihood for treatment success is relatedto the time required to initiate effective antimicrobial therapy. Atbest, confirmatory tests now require at least one day to perform withisolate culturing. In contrast, automated microscopy has the potentialto reduce turnaround time by detecting complex resistance phenotypesdirectly in positive culture broth. The purpose of our study was todetermine the sensitivity, specificity, and speed of automatedmicroscopy to detect ESBL expression in clinically significant isolatesof Enterobacteriaceae.

Materials and Methods

Direct observation of microorganism response to antibiotic exposure wasperformed on a disposable 32-channel fluidic cassette (FIG. 4A) insertedinto a custom bench-top evaluation system in accordance with variousembodiments of the present disclosure that combines digital microscopy,motion control, and image analysis. The system was used to measuregrowth of immobilized bacteria in a multichannel fluidic cartridge.Cassette flowcells were constructed with transparent top and bottomsurfaces to allow microscope imaging.

Multiple institutions provided clinical strains. The collection included24 strains of Enterobacteriaceae known to be ESBL-positive, and 32 knownto be ESBL-negative. Strains included K. pneumoniae (14 neg, 11 pos); K.oxytoca (1 neg); E. coli (12 neg, 7 pos); P. mirabilis (1 pos); E.cloacae (3 neg, 4 pos), E. aerogenes (1 pos); S. marcescens (1 neg); andC. freundii (1 neg). Quality control strains included CLSI standardstrains from the ATCC. CLSI ESBL confirmatory disk diffusion tests (DD)served as the comparator.

10 mL of banked whole blood in CPD was spiked into BACTEC Plus Aerobic/Fbottles (BD), followed by an isolate spike using the above-listedclinical isolates to make 103 CFU/mL final concentration. Bottlesincubated overnight (16 hours) at 35° C. with agitation. Centrifuged 100μL culture aliquots were resuspended in 500 μL of a low ionic strengthbuffer to lyse blood cells. Further 5,000-fold dilution used anelectrokinetic buffer to produce inocula. 20 μL buffer-resuspendedaliquots of the inocula were pipetted into each flowcell andelectrokinetic concentration captured live cells onto the flowcellsurfaces. Antibiotics were then introduced into each flowcell. ESBLdetection used 4 separate fluidic channels, each receiving a 20 μLinoculum containing 256 μg/mL of ceftazidime (CAZ) or cefotaxime (CTX)with or without 4 μg/mL of clavulanic acid (CA). Total preparation timeaveraged 20 minutes prior to placing the cassette on the microscopestage. Each 20× magnification field of view for microscopy containedapproximately 4 to 40 growing clones, and each flowcell channelcontained 40 separate fields of view. The system acquired darkfieldimages of 40 separate fields of view for each condition (each flowcell)every 10 minutes for 3 hours, computed the mass of each channel's cellpopulation during the test, and compared the mass ratio in theantibiotic-only to its paired +CA channel. Thus, the system performed 6concurrent assays in separate flowcells for each isolate, summarized inTable 3, below:

TABLE 3 Assay conditions for growth evaluation and detection of ESBLpositive blood cultures using MADM system. Growth control (no drugs)Clavulanic acid (CA) 4 μg/mL Ceftazidime (CAZ) 256 μg/mL CAZ-256 + CA-4Cefotaxime (CTX) 256 μg/mL CTX-256 + CA-4

The clone-by-clone growth analysis was performed for each isolate undereach test condition using the image analysis algorithm. The systemclassified ESBL status by computing the growth in each drug with orwithout CA, using the ratio of clone mass. The system classified anisolate as ESBL positive if the mass ratio achieved a threshold,compound criteria using both drugs. The comparator was a CLSIconfirmatory ESBL disk diffusion (DD) assay.

The MADM system correctly classified 24 of 24 ESBL-positive strains(100% sensitivity), and 30 of 32 ESBL-negative strains (94% specificity)within three hours after inoculum introduction. The average maximum massratio of ESBL-negative isolates was 2.4±5.4 s.d. while the average forESBL-positives was 96±113 s.d. Sensitivity was 100% (CI 84-100%) andspecificity 94% (CI 78-99%). MADM system results are summarized in Table4.

TABLE 4 MADM system results for ESBL assays. ESBL− ESBL+ MADM+ 2 24 26MADM− 30 0 30 32 24 56 Overall Result Sensitivity 100% CI 84-100%Specificity  94% CI 78-99% 

FIG. 11 illustrates growth curves for one positive ESBL+ (E. cloacae),upper curves, and one negative ESBL− (E. coli), lower curves, strainidentified using the MADM system. The data is illustrated as mass ratiosbetween drug alone (ceftazidime (CAZ) or cefotaxime (CTX)) and drug (CAZor CTX) with CA. FIGS. 12A-12C illustrate images at hourly intervals ofan ESBL-positive strain K. pneumoniae ESBL+. Rows show clone images at60-minutes intervals starting after a 20-minute delay to first imageduring antibiotic exposure. FIG. 12A illustrates drug-free K. pneumoniaeESBL+ growth control. FIG. 12B illustrates K. pneumoniae ESBL+ growth inthe presence of CAZ. FIG. 12C illustrates K. pneumoniae ESBL+ growth inthe presence of CAZ+CA. A 20 μm scale bar is shown (lower right). FIG.13 illustrates receiver operating characteristic (ROC) for the rulesused in testing. FIG. 14 illustrates scatter plot of mass ratios for thetwo agents (CAZ or CTX) in the presence and absence of CA with allstrains. Two false positives occurred (FP). One (A) had marginal diskzone differential (4 mm) and the other (B) exhibited delayed drugeffects. Squares indicate ESBL positives by DD, and dots indicate ESBLnegatives. The dashed box outlines the original interpretation criterionzone.

One false-positive occurred for a K. pneumoniae strain that had DDvalues of 11 and 15 mm, just below the 5 mm difference required, andproduced microcolonies in the zones. It is indicated as datum “A” inFIG. 14. An E. coli strain, indicated as strain “B” in FIG. 14, wasclearly negative by DD but had an initial CAZ growth difference thatdisappeared after 90 minutes. This suggested a rule change to compensatefor potentially delayed drug action.

Conclusions

This study demonstrates direct 3-hour blood culture pathogen ESBLresistance phenotype detection using automated microscopy. It extendsother MADM system studies that used respiratory specimens and additionalresistance phenotypes. Direct measurement of the magnitude and kineticsof clavulanate synergy enabled sensitive, specific, and rapid detectionof the ESBL phenotype using a single challenge concentration of eachantibiotic. The analytical speed of the automated system was consistentwith that required to help de-escalate empiric therapy in critically illbacteremic patients.

Example 4 Same-Day ID and Resistance Phenotyping Directly fromRespiratory Specimens by Automated Microscopy Introduction

Nosocomial infections due to multiple drug resistant (MDR) bacteria areincreasing in frequency and growing in complexity. For critically illpatients, resistance can render initial therapy ineffective, delayingthe start of effective antimicrobial therapy. But standard diagnosticcultures introduce a 2-3 day delay to provide guidance. Rapid, same-day,direct-from-specimen ID and AST of respiratory specimens could reduceclinical morbidity and mortality. Systems and processes for evaluatingmicroorganism in accordance with various embodiments of the presentdisclosure, such as a multiplexed automated digital microscopy (MADM)system described and used in the following example, have the potentialto reduce turnaround time by rapidly analyzing bacteria extracteddirectly from a clinical specimen. A pilot study using respiratoryspecimens was performed to compare analysis performed using amultiplexed automated digital microscopy (MADM) system with traditionalcultures-based approaches. The purpose of our study was to determine thespeed and accuracy of a MADM system as an alternative to culturing withsame-day quantitation, identification, and resistance phenotyping.Customized MADM systems used commercial inverted microscopes with 12-bitmonochrome cameras. A computer system ran custom image analysis andexperiment control software. 32-channel disposable cassettes (FIG. 4A)enabled live microbial cell immobilization for microscopy and fluidexchanges for different test media and reagents.

A total of 281 de-identified remnant respiratory specimens werecollected from hospital and commercial sources. The specimens included25 endotracheal aspirates (ETA) of unknown age obtained from a specimenvendor. Also included were 230 mini-bronchoalveolar lavage (mini-BAL)and 26 ETA specimens obtained from Denver Health Medical Center (DHMC).DHMC specimens were 7-21 days old. AST results were available for allmini-BAL specimens but not for ETA. Accompanying reports also includedsemi-quantitative ID used to select 92 bacteria-positive specimens foranalysis using the MADM system. Targets included Pseudomonas aeruginosa,Acinetobacter baumannii complex, and Staphylococcus aureus. Controlsused standard culturing methods (Cx).

After culture re-test, 79 specimens demonstrated quantitative culture(qCx) values above the diagnostic threshold (1e4 for mini-BAL and 1e5CFU/mL for ETA). These specimens were prepped for introduction into theMADM system. The system performed quantitative ID for Staphylococcusaureus (STAU), Pseudomonas aeruginosa (PSAE), and Acinetobacter spp.(ABCC). Concurrent quantitative culture was performed on specimens. TheMADM system performed resistance phenotype tests on STAU-containingspecimens for cefoxitin (FOX) MRSA phenotyping and clindamycin (CLI)resistance. The system tested ABCC- and PSAE-containing specimens foramikacin (AN) and imipenem (IMP) resistance.

Specimen preparation used a brief procedure to release bacteria, reduceimaging background, and suspend bacteria in a low ionic strength buffer.We rejected 13 specimens reported as positive but for which repeat qCxfailed to confirm content. We rejected 10 samples with heavy interferingbackground when dilution to OD₆₀₀=0.3 yielded organism counts inadequatefor analysis. We rejected 7 samples for other technical deficiencies.The remaining 62 specimens were tested using the MADM system.

20 μL samples of prepared specimen were pipetted into independentflowcell channels and a low-voltage electrical field was applied for 5minutes. The electrical field concentrated bacteria onto a functionalsurface coating that immobilized the bacteria on the lower flowcellsurface. Each flowcell channel received only one type of test reagentsolution that contained a selective agent if required (only for channelsused to test AB, sulbactam 32 μg/mL).

The instrument acquired images at 10-minutes intervals for 180 minutes,using 10 fields of view in each flowcell channel through a 20×objective. Imaging used darkfield illumination. Identification variablesincluded response to selective agents (AB with sulbactam), cellmorphology, growth morphology, and growth rate.

Identification algorithms applied to each individual immobilizedbacterial cell. The system measured the amount of change in mass overtime to compute growth rates. Identification consisted of computing andcombining probability scores for morphology, response to selectivemedia, and growth rates to produce a receiver operating characteristiccurve (ROC) to derive classification criteria.

Results

FIG. 15 illustrates organism growth after one hour to exemplifymicroscopy images used for time-lapse quantitative analysis. 80% ofspecimens had multiple species, but none had multiple target species.FIG. 15A illustrates SA growth at 0 and 60 minutes. FIG. 15B illustratesPA growth at 0 and 60 minutes. FIG. 15C illustrates AB (AB in 32 μg/mLsulbactam) growth at 0 and 60 minutes. FIG. 15D illustrates KP(Klebsiella pneumonia, non-target) growth at 0 and 60 minutes.Non-growing pixel blobs are considered debris.

Dark field illumination revealed specimen matrix residue pixel blobswith a broad range of size and morphology. The system distinguished livemicroorganisms by requiring measurable growth as well as morphologiccriteria in accordance with various aspects of the present disclosure.

MADM system results were concordant with repeat qCx in 59/62 specimens.Identification scoring algorithms for STAU yielded 14/14 true positives(TP), 45/45 true negatives (TN), 2 false positives (FP), and 1 falsenegative (FN); for ABCC 1/1 TP, 60/61 TN, 1 FP; for PSAE 3/3 TP, 59/59TN. Two specimens yielded false positives for STAU and one yielded aSTAU false negative. Overall ID performance was 95% sensitivity and 99%specificity. 2 specimens had STAU that expressed the MRSA phenotype byMADM (FOX) and Cx (OXA), and one by MADM system analysis only. None ofthe STAU expressed CLI-resistance. All PSAE and ABCC were susceptible toIMP and AN. MADM system resistance detection was concordant withhospital AST results except with one STAU sample (MSSA by oxacillin MIC,MRSA by FOX in the MADM system). For the 186 ID tests, Table 5summarizes performance. Times to results were 1 hour for specimen prepand 3 hours to all analytical results for a total of 4 hoursspecimen-to-answer. Table 6 summarizes resistance phenotype results,with one discordant MRSA false positive.

TABLE 5 ID performance, MADM vs. Cx. Sensitivity 95% CI95 = 73%-100%Specificity 98% 94%-100% Positive Predictive Value 86% 64%-96%  NegativePredictive Value 99% 96%-100% Positive Likelihood Ratio 53 NegativeLikelihood Ratio 0.05

Table 6. Target-positive specimens.

Cx MADM Cx MADM Cx MADM Cx MADM SPECIMEN TYPE ID CLI CLI MRSA MRSA AN ANIMP IMP DH 425 Mini-BAL STAU S S S R DH 427 Mini-BAL STAU S S S S DH 439Mini-BAL STAU S S R R DH 440 BAL STAU S S S S T12 ETA STAU S S R R ETA280667 ETA STAU NA NA NA NA DH 457 BAL STAU S S S S DH 485 BAL STAU S SS S DH 500 BAL STAU NA NA NA NA DH 509 BAL STAU S S S S DH 514 Mini-BALSTAU S S S S DH 543 BAL STAU S S S S DH 550 BAL STAU S S S S DH 556Mini-BAL STAU S S S S DH 430 Mini-BAL PSAE S S S S DH 554 BAL PSAE S S SS DH 641 Mini-BAL PSAE S S S S ABCC NA S S S NA = not analyzed: STAU =S. aureus: PSAE = P. aeruginosa: ABCC = Acinetobacter sp.: CLI =clindamycin: AN = amikacin: IMP = imipenem: Cx = culture result

Discussion

2 false positive STAU IDs resulted from incorrect speciation (1 chainedcocci, 1 Enterococcus). Test optimization or fastidious media couldimprove future versions. One ABCC false positive was Enterobacter sp.The false negative STAU had too few clones to meet the call criterion.Scanning more fields of view resolves this problem. The MRSA discordancearose in Cx with oxacillin, which is no longer considered the mostreliable phenotyping agent (FOX, as used in the MADM system analysis).The small number of cells required for analysis is compatible with thebacterial concentration at BAL diagnostic threshold of 10⁴ CFU/mL andETA at 10⁵ CFU/mL.

Conclusions

A multiplexed automated digital microscopy system in accordance withvarious embodiments of the present disclosure accurately analyzed liveimmobilized bacteria extracted directly from mini-BAL and ETA specimens.Total specimen-to-answer time was 4 hours.

Example 5 Automated 4-Hour Detection of HeteroresistantVancomycin-Intermediate Staphylococcus aureus (hVISA) Introduction

Infection with heteroresistant organisms can be difficult or impossibleto detect by standard antibiotic susceptibility culturing methods usingMIC criteria. Of particular concern, heteroresistance by S. aureus tovancomycin (VAN) may be emerging as a diagnostic challenge. Themagnitude of the problem remains obscure because VAN-heteroresistant S.aureus (hVISA) exhibits MICs within the susceptible range but may leadto VAN failure. This leaves the microbiological laboratory communityunable to perform adequate epidemiological and clinical studies. Thepurpose of our study was to determine assay criteria for multiplexedautomated digital microscopy (MADM) system to rapidly identify the hVISAphenotype in individual live organisms using abbreviated populationanalysis profiles (PAP). Numbers of individual organisms tested fellwithin the range obtainable directly from lower respiratory specimens.

Materials and Methods

Customized MADM systems in accordance with various embodiments of thepresent disclosure used commercial inverted microscopes with 12-bitmonochrome cameras. The computer-based system ran custom image analysisand experiment control software, as described herein. 32-channeldisposable cassettes (FIG. 4A) enabled live microorganism immobilizationfor microscopy and fluid exchanges for different test media andreagents.

We characterized Staphylococcus aureus (SA) clinical isolates along withisolates from a Centers for Disease Control (CDC) SA collection using48-hour broth microdilution abbreviated population analysis profiles(BMD-PAP), which served as the control. A total of 30 isolates werecharacterized. We also applied BMD-PAP to hVISA reference strain Mu3(ATCC 700698), and measured areas under the curve (PAP-AUC) in alltests.

BMD-PAP consisted of serial isolate concentrations from 1 to 106 CFU/mLdropped onto sectors of VAN agar plates containing from 0 to 6 mg/mL in10 steps (non-doubling dilutions) and counting colonies. An isolate metthe hVISA+ detection threshold criterion if its BMD-PAP-AUC≧0.9 Mu3 AUC.This study did not attempt to discriminate between hVISA and VISA, asdesignated by the plus sign in hVISA+.

For MADM system analysis, each independent flowcell channel, an exampleof which is illustrated in FIG. 4B, contained 10 μL of 106 CFU/mL(pipette introduction). A capture coating immobilized individualmicroorganisms for microscope image acquisition and spatial mapping. Awash displaced the sample fluid with Mueller-Hinton media containingdifferent VAN concentration for each flowcell, from 0.01 to 4 μg/mL,plus one antibiotic-free growth control channel. The system evaluatedchanges in mass of each growing clone in accordance with variousembodiments of the present disclosure. Each treatment flowcell channelreceived one of the following VAN concentrations: 0, 0.01, 0.05, 0.1,0.2, 0.4, 0.8, 1.0, 2.0, or 4.0 μg/mL.

The instrument acquired images at 10-minutes intervals for 90 minutes,using three fields of view in each flowcell channel through a 20×objective and darkfield illumination. In each channel, the systemapproximately 1,000 growing clones. It then counted the number of clonesfrom the same population sample that exhibited at least 4-fold gain inmass by the end of a 4-hour analysis period. Computation normalized thelatter count by dividing it by the initial count. The abbreviated AUCwas also determined and compared to microdilution PAP AUC.

By plotting abbreviated PAPs for these normalized counts, we thenselected an AUC value for the abbreviated PAP region that yielded thebest discrimination between hVISA+ and VSSA strains determined byBMD-PAP AUC and the Mu3 reference AUC.

Results

BMD-PAP detected 15 hVISA+ isolates (3 CDC strains, 12 screened clinicalisolates) and 15 VSSA (12 CDC strains). One MADM system evaluation witha VSSA strain contained too many organisms to count and was censored asa technical error, leaving 29 total comparisons. The MADM systemcorrectly classified 14/15 hVISA+ strains, and 14/14 VSSA strains. Asillustrated in the plot of MADM-PAP-AUC vs. BMD-PAP-AUC (arbitrary unitsfor areas) (FIG. 16), one discrepant hVISA+ strain exhibited a MADMsystem AUC value below the classification criterion. Horizontal dottedline shows the MADM system criterion AUC level, and vertical dotted lineshows BMD-PAP AUC detection criterion. Test sensitivity was 93% (CI9566%-100%) and specificity was 100% (CI95 73%-100%). The MADM systemtime-to-result was 4 hours.

FIG. 17 illustrates an example of an hVISA+ strain at several VANconcentrations used to derive the abbreviated MADM system PAP. The topimage illustrates hVISA+ in the presence of 0 μg VAN, the second image(from top) illustrates hVISA+ in the presence of 0.4 μg VAN, the thirdimage (from top) illustrates hVISA+ in the presence of 1.0 μg VAN, thefourth image (from top) illustrates hVISA+ in the presence of 2.0 μgVAN, and the bottom image illustrates hVISA+ in the presence of 4.0 μgVAN. Scale bar shown in upper left image. Increase in individual clonemass appears as a brightening of, and increase, in the clone's2-dimensional footprint area. Integrated pixel intensity enablescomputation of growth rates over time using a series of time lapseimages acquired at 10-minute intervals. A count of 4-hours clones thatexhibit at least 4-fold growth, divided by the initial count of growingclones in the same fields of view, yielded normalized PAPs.

Discussion

Growth analysis by the MADM system revealed an identification criterionfor using abbreviated PAPs of individual clones growing in the presenceof different VAN concentrations to identify non-susceptible S. aureussubpopulations in isolates obtained from various sources. The comparatormethod used an analogous PAP with broth cultures and the generallyaccepted classification criterion against a stable reference strain(Mu3).

MADM system PAPs for positive strains had down-sloping characteristicsof heteroresistance as did the BMD-PAPs. This study identified a narrowrange of VAN concentrations to use for expanded studies, enablingefficient and rapid automation. At its present state, the MADM systemappears applicable for use with clinical isolates to identify hVISA+within 4 hours. This enables replication with larger screening studiesto help estimate phenotype prevalence as well as characterizingstatistical performance.

The small number of cells required is also compatible with the numberavailable from lower respiratory tract specimens at the diagnosticthreshold. Additional research with polymicrobial specimens willdetermine potential for inclusion in a practical rapid diagnosticsystem.

Conclusion

A multiplexed automated digital microscopy (MADM) system in accordancewith various embodiments of the present disclosure identified hVISA+isolates in 4 hours with 93% sensitivity and 100% specificity in acollection of 29 isolates characterized by a broth microdilution methodsfor population analysis profiling.

Example 6 Rapid Microbiological Identification and Major Drug ResistancePhenotyping Using a Multiplexed Automated Digital Microscopy (MADM)System for Ventilator-Associated Pneumonia (VAP) SurveillanceIntroduction

Standard clinical VAP diagnosis is imprecise, with subsequent treatmentoften delayed and associated with increased morbidity, mortality (28-dMR=30%) and hospital costs. Quantitive culture (qCx) of bronchoalveolarlavage (BAL) is usually obtained only after VAP is clinically diagnosed.Surveillance of at-risk mechanically ventilated (MV) adults withmultiple BALs is associated with significantly more antibiotic-free days& fewer deaths. However, surveillance qCx requires 48-72 hours forresults from conventional labs. Susceptibility testing requires anadditional day.

Surveillance microbiological testing for rapid bacterial identificationand antibiotic resistance testing was evaluated using a multiplexedautomated digital microscopy (MADM) system in accordance with variousembodiments of the present disclosure to assess whether it couldsensitively identify patients who subsequently develop VAP when comparedto usual microbiological approaches using conventional culture methodsof lower respiratory samples from patients at risk for VAP and reducetime to initiation of treatment and reduce failure rates of initialtherapy.

Materials and Methods

Adult MICU patients with identified surrogate were included within 72hours of intubation and if anticipated to require MV for >48 h. Moribundstate or pregnancy were exclusions. Surveillance mini-BAL (Combicath,Plastimed) was performed on Day 1, 3, 5, 7 and 10 of MV. Samples weresplit and processed for both a) routine respiratory quantitativemicrobiological culture and sensitivity assays (>48 h resultavailability) and b) rapid (<8 hour) flowcell/surface-capture assaysusing the MADM system. Viable microorganisms were identified usinggrowth analysis enhanced by a focused VAP antibody panel (S. aureus, P.aeruginosa, A. baumannii). Untypable organisms were also reported.Sensitivity was assessed using growth analysis. Bacterial species andantimicrobial agent resistance mechanisms are summarized in Table 7.Attending physicians were blinded to MADM system results.

BAL sample were prepared, removing debris and separating microorganismsfrom other sample material. Sample microorganisms were introduced into amultichannel fluidic cassette as described elsewhere herein. Bacteriawere concentrated and retained on the lower surface of all flowcellsusing low-voltage electrical field (5 min) Antibody labeling of bacteriain flowcells was used to aid identification.

The automated digital microscopy system was used to perform darkfieldimaging of 10 fields of view every 10 minutes for 180 minutes in eachflowcell channel. Initial epifluorescence imaging was also performed forantibody detection. The system used identification algorithms inaccordance with various embodiments of the present disclosure for eachindividual immobilized microorganism exhibiting growth and determinedgrowth rates of progeny for the duration of the test. Identificationconsisted of probability scores based on microorganism morphology andgrowth rates. Antibody labeling identification data was alsoincorporated. Observed microorganisms were then classified as STAU,PSAE, or non-target. Antibiotic responses were used to aididentification when appropriate. Quantitation was performed by countingidentified microorganisms and computing original specimen CFU/mL.

Conventional clinical microorganism identification was performed by DHMCmicro lab using standard CLSI procedures. Clinical microbiological datawas provided to ICU clinicians for medical decision making.Identification information generated using the MADM system wasprospectively performed but not available for clinical decision making.

TABLE 7 Antimicrobial agent resistant mechanisms of various bacterialspecies. Species Resistance Mechanism S. aureus (STAU) MRSA phenotypeClinda resistance (any) P. aeruginosa (PSAE) Amikacin resistancePiperacillin/Tazobactam resistance A. baumannil (ABCC) Imipenemresistance Cefepime resistance VAP target organization panel foridentification & susceptibility >10⁴ CFU/ml

Primary outcome assumptions were made relative to study power and samplesize: 1) 10% incidence of VAP, 2) 40 h difference in clinicallyreportable VAP target (QCx BAL ID (48 h)+resistance (18 h) vs. MADMsystem determination of BAL ID (4 h)+resistance (2 h)), and 3) 80%power, two-tailed α≦0.01 requires 35 patients, assuming a median of 2mini-BAL per patient (˜8 unique isolates).

Results

A total of 77 mini-BALs (median 2; range 1-7 per patient) were performedon 33 MV patients. Patient demographics and BAL safety and surveillancestatistics are presented in Tables 8 and 9. Study results are summarizedin Tables 10-12. 20 (61%) patients had diffuse or patchy chest x-rayinfiltrates and 3 patients had no infiltrates on enrollment. 70 BALsamples were tested using a MADM system. FIG. 18B illustratescomputer-imposed ellipses to indicate potential microorganisms (pixelblobs, FIG. 18A) tracked for growth during analysis. 12 samples grew ≧1bacterial type at >104 CFU/mL by qCx. 8 samples contained mixedrespiratory bacteria. 7 samples contained VAP associated bacteria (4 S.aureus (including 1 auxotroph and 3 MRSA), 2 S. maltophilia, 1 K.pneumoniae). The MADM system identified 3 of 4 target organismsaccurately and antimicrobial response enabled identification of 2 of 2S. malttophilia. A K. pneumoniae sample was reported untypable.Auxtrophic growth precluded testing for 1 S. aureus sample.Antimicrobial response matched in 5 samples (3 MRSA, 2 S. maltophilia).14 samples grew ≧1 bacterial type at <104 CFU/mL by QCx. 10 samplescontained mixed respiratory bacteria, 3 samples yeast, 2 samples lactosefermenting GNB, 1 sample non lactose fermenting GNB, 1 sample H.influenzae, beta lactamase positive, 1 sample H. species, notinfluenzae, 1 sample Beta hemolytic Streptococcus. None of the patientshaving bacteria detected by QCx at <104 CFU/mL developed clinical VAP.In 98% of samples, MADM system determined results were concordant withQCx-negative samples. The MADM system detected an enteric organism (10⁵)in one sample negative by QCx. One VAP was diagnosed by clinicalcriteria. MADM system determined identification would have resulted inimportant and earlier antibiotic change/addition in 63% of mini-BALsamples with above threshold target organisms by QCx.

TABLE 8 Patient demographics (n = 34) Age; Median (IQR)  55 (41-60)Gender 21M: 13F Ethnicity Hispanic 14 (42%) Native American 1 (3%)Caucasian 14 (42%) African American  4 (12%) Smoking Ever 27 (82%)Current 17 (52%) Alcohol Use AUDIT Score Median (IQR)  7 (0-18) APACHEII Median (IQR)  21 (16-24) Mech. Vent (days) Median (IQR)  4 (6-10) ICULOS (days) Median (IQR)  10.5 (6.5-18.2) ICU D/C Status Deceased 11(33%) Home 18 (55%) SNF 3 (9%) T/F - acute hospital 1 (3%)

TABLE 9 BAL surveillance and safety Patients enrolled 34 Surveillancemini-BAL performed 77 Combicath (Plastimed) 66 AirLife ™ Catheter(Carefusion) 11 BAL per patient; Median (IQR, range) 2 (1-4, 1-7) BALreturn; Average (SEM) 5.2 ± 0.5 mL Surveillance BAL Adverse Events Total(BALs = 77) n % Desaturation requiring increase Fi02 2 3% Tachycardia 11% Agitation post mini BAL (60 min) 2 3% Bloody return 4 5% Total 9 12% 

TABLE 10 Microbial ID; clinical correlations MADM Micro ID ConventionalMicro ID 4-6 Hurs (BACcel ™) 48-72 hours Conc Phenotype, Con- Abx attime DC Spec# CPIS BACcel ID (CFU/ml) sensitivity Isolate cordance ofmini-BAL Status 003- 4 Fastidious 1.07 × 10⁴ Phenotype not 10⁴⁻10⁵ noNone SNF D1 Organism assessed MSSA 005- 3 Enteric 1.26 × 10⁵ AN, IMP -no No isolate no CTX D5 Died D7 growth, CAZ, not on CLI, FOX, TZP - dayall growth 006- 6 Steno 7.68 × 10⁵ AN, CAZ, CLI, >10⁵ yes Vanco/ Home D1FOX, IMP - all S. maltophilia lcaspo/ growth, TZP- imipenemantimicrobial effect 006- 9 Steno 1.60 × 10⁴ AN, CAZ, CLI, 10⁴-10⁵ yesTMP/ Home D3 FOX, IMP - all S. maltophilia Levaquin/ growth, TZP- Casp/antimicrobial Vanco effect 008- 9 STAU 1.11 × 10⁶ FOX - R >10⁵ yesMetronid Died D7 (MRSA) CLI-R MRSA azole only 008- 9 STAU 1.42 × 10⁵Technical 10⁴-10⁵ yes Metronid Died D10 failure, no MRSA azole phenotypeonly 017- 7 UNK/enteric 1.87 × 10⁴ ID UNK, no 10⁴-10⁵ yes Vanco, SNF D1phenotype K. pneumo HIV available 022- 8 STAU 4.00 × 10⁴ MRSA 10⁴-10⁵yes Vanco; Home D3 MRSA (zosyn DC d2) 033- 6 STAU 6.64 × 10⁴ MRSA, CLI-R10⁴-10⁵ no Cefepime, Home D7 Candida spp. Vancoflouc;

TABLE 11 Microbiology performance Performance Characteristic RateComments BAL Samples with Target 12 (15.6%) 9 patients organism micro IDConcordance Conventional 7 of 8 vs. CPIS => 6 Concordance BACel 8 of 9vs. CPIS => 6 BACel call prior to routine 9 of 9 Change of Abx in 6;change care Abx stopped in 2 VAP diagnosis by CDC NIS 1 Entericorganism* criteria *Organism not specified. BACcel positive on D7. Noantibiotic for 2 days at time of miniBAL; Patient died.

TABLE 12 Study results. Clin Micro Presense/ STAU, PSAE, Absence ≧1 ×10⁴ CFU/mL ABCC, Steno, Positive Negative Enteric BACcel Positive TruePositive False Positive Positive MADM (N = 6) (N = 2*+) Predictive valueNegative False Negative True Negative 75% (6/8) (N = 1+) (N = 61)Negative Sensitivity = Specificity = predictive value 86% (6/7) 97%(61/63) 98% (61/62) *Patient with diffuse infiltrates + clinicalpneumonia CPIS score (≧6) +BACel isolate: Gr + ve clustered cocci.Speciation pending S. auerus vs. CNS +STAU grew in fastidious growthmedia flow cell but STAU ID test was not activated in that flow cell.STAU was called correctly after activation POSITIVE diagnosticlikelihood ratio (+DLR) = 27:95% CI [6.7-109] NEGATIVE diagnosticlikelihood ratio (−DLR) = 0.15:95% CI [0.02-0.91]

Conclusions

Mini-BAL based surveillance for VAP is both feasible and safe inventilated at-risk patients. MADM system-based microbiologicalsurveillance for VAP demonstrated sensitivity (86%) and specificity(97%), with a significant reduction in time to clinically availablebacterial ID and resistance (approx 40-66 hr lead time) for multipleorganisms and resistance types. In 5 of 7 (63%) mini-BAL samples with atarget organism above threshold by QCx, MADM system-based ID would haveresulted in important and earlier antibiotic changes/additions. As shownhere, systems in accordance with various embodiments of the presentdisclosure, such as the MADM system, is a promising approach for rapidsurveillance in patients at risk for VAP.

Example 7 Rapid Identification of Resistance Phenotypes in Gram-NegativeBacilli Using an Automated Digital Microscopy System Introduction

Nosocomial infections due to multi-resistant Gram negative bacteria areincreasing in frequency and growing in complexity. Pseudomonasaeruginosa (PA) and Acinetobacter baumannii (AB) are major causes ofnosocomial infection and difficult to manage because of multi-drugresistance. Enterobacteriaceae that acquire the KPC carbapenemase arealso likely to co-exist with multi-drug resistance in addition topresenting formidable detection challenges. Conventional phenotypingmethods require growth of large numbers of bacteria, which increases thetotal time-to-result. For critically ill patients, the likelihood forsuccess is indirectly related to the time required to administereffective antimicrobial therapy. However, standard tests require 2-3days to characterize antimicrobial resistance patterns usingculture-based methods. In contrast, various systems and methods inaccordance with various aspects of the present disclosure, such asmultiplexed automated digital microscopy (MADM) systems, have thepotential to reduce turnaround time by direct detection of antimicrobialresistance phenotypes in bacteria extracted from a clinical specimen.The purpose of this study was to determine the sensitivity, specificity,and speed of automated microscopy to detect major resistance phenotypesassociated with multi-drug resistance in significant Gram-negativeclinical isolates.

Materials and Methods

A MADM system was used with a purpose-built 32-channel disposablefluidic cassette (FIG. 4A). Cassette flowcells (FIG. 4B) had transparenttop and bottom surfaces to allow microscope imaging. The bottom surfacewas coated with poly-L-lysine to immobilize live bacteria.

Clinical isolates of Pseudomonas aeruginosa (PA), Acinetobacterbaumannii (AB), and Klebsiella pneumoniae (KP) were tested. Test agentsincluded amikacin (AN), imipenem (IMP), ceftazidime (CAZ), ertapenem(ETP), aminophenylboronic acid (APB), and benzo(b)thiophene-2-boronicacid (BTB). The boronic acids inhibit the KPC enzyme as well as AmpC.Table 13 summarizes organisms and test conditions. Test results areexpressed as nonsusceptible (NS) or susceptible (S).

TABLE 13 Organisms and test conditions Species Test # NS/S Conditions PAAN 37/35 AN 32 μg/mL AB IMP 26/66 IMP 8 μg/mL CAZ 59/17 CAZ 8 μg/mL KPETP  6/13 ETP 16 μg/mL KPC/APB ETP 16 μg/mL + APB 300 μg/mL KPC/BTB ETP16 μg/mL + BTB 50 μg/mL

Isolates were grown on blood agar, suspended colonies in tryptic soybroth for 2 hours, then centrifuged and resuspended log-phase bacteriain low ionic strength electrokinetic buffer.

MADM system analysis was performed using a 32-channel disposable fluidiccassette (FIG. 4A) to measure growth of immobilized bacteria. PAclinical isolates were separately tested for nonsusceptibility (NS) withamikacin (AN) at 32 μg/mL, and AB isolates with imipenem (IMP) at 8μg/mL or ceftazidime (CAZ) at 8 μg/mL. Klebsiella pneumoniae (KP)clinical isolates were tested for ertapenem (ETP) nonsusceptibility at16 μg/mL with and without the inhibitors aminophenyl boronic acid (APB)at 300 μg/mL or benzo(b)thiophene-2-boronic acid (BTB) at 50 μg/mL toidentify putative KPC-positive strains.

10 μL aliquots of 5E+7 CFU/mL were pipetted into separate flowcells foreach isolate and test condition. Microorganisms were electrokineticallyconcentrated onto the flowcell detection surface with an electricalfield to the positively charged lower surface to immobilize cells andyield 10-100 bacteria per field of view (FIG. 4B). Each isolate wastested in separate flowcell channels with no antibiotic (growthcontrols). The system measured growth at 10-minute intervals.Microorgansims were exposed to the test conditions for 3 hours. Growthwas evaluated, interpreting results for PA, AB, and KP-ETP as NS ifgrowth had not arrested within 3 hours. Results for both KP-KPC testswere interpreted as presumptive for KPC if growth differences betweenthe inhibited (with APB or BTB) and uninhibited (ETP alone) exceeded acriterion amount (FIG. 21) in the same 3 hour time frame. CLSI diskdiffusion assays were performed as controls for PA and AB; and a CLSIHodge Test and RT-PCR were performed as controls for KP.

Results

Table 14 summarizes assay performance. Sensitivity and specificity were,respectively: PA-AN (33/37) 89% and (33/35) 94%; AB-IMP (24/26) 92% and(65/66) 98%; AAB-CAZ (58/59) 98% and (14/17) 82%; KPETP (6/6) 100% and(13/13) 100%; KPC/APB (5/6) 83% and (13/13) 100%; KPC/BTB (4/6) 67% and(13/13) 100%.

TABLE 14 Assay performance Test Sensitivity Specificity PA-AN 33/37 89%CI 74-96% 33/35 94% CI 79-99% AB-IMP 24/26 92% CI 73-99% 65/66 98% CI91-100% AB-CAZ 58/59 98% CI 90-100% 14/17 82% CI 56-95% KP-ETP 6/6 100%CI 52-100% 13/13 100% CI 72-100% KP-KPC/APB 5/6 83% CI 36-99% 13/13 100%CI 72-100% KP-KPC/BTB 4/6 67% CI 24-94% 13/13 100% CI 72-100%

FIGS. 19A and 19B illustrate images of non-fermenter clinical isolatesexposed to antibiotics for 3 hours. Images zoomed in for detail. Sum ofintegrated pixel intensities of individual clones closely parallelsclone mass and/or cell count from standard methods. Either susceptible(FIG. 19A, S) or resistant (FIG. 19B, R) strains may show abnormalmorphology during growth in drug-containing media. The detectabledifference occurs when a susceptible strain ceases to grow.

FIGS. 20A-20C and FIG. 21 illustrate images and growth data from KPtests. The left panel illustrates 20 min after start of drug exposure.The right panel illustrates 90 min after start of drug exposure. Imagesare zoomed for detail. ETP exposure causes abnormal growth morphology.The sum of integrated pixel intensities of individual clones closelyparallels clone mass and/or cell count. The difference between ETP alone(FIG. 20B) and ETP+APB (FIG. 20C) shows enzyme inhibition and decreasedresistance, hence KPC-positive interpretation. Clear differences occurbetween the behaviors in ETP alone (FIG. 20B) and ETP with an enzymeinhibitor added (FIG. 20C). Images show the same field of view atdifferent times (20 and 90 minutes of drug exposure).

FIG. 21 illustrates KPC assays, growth rate vs. exposure time.“GC”=growth control, one for KPC-positive strain (same as in FIG. 20)and one for KPC-negative strain (dotted line). For KPC-positive, thedifference between growth curves for ETP alone and ETP with a boronicacid enzyme inhibitor (APB or BTB) determines the interpretation.Susceptible strains show no differences, and also fail to grow in ETP at16 μg/mL (lower dotted line). The dashed arrows indicate growth curvesthat exceeded difference criteria (from the heavy dashed arrow) for apositive interpretation (enzyme positive ETP alone vs. ETP+inhibitor).

Conclusions

Direct analysis of small numbers of bacteria using ADM identifiedresistance phenotypes in non-fermenters and in K. pneumoniae within 3hours. The experimental method met the objectives of using a smallnumber of cells, achieving rapid results, and having accuracyapproaching those of standard tests in identifying major resistancephenotypes, including difficult-to-detect KPC-positive organisms. Cellnumber was consistent with that previously shown adequate to rapidlyidentify pathogens from organisms extracted directly from apolymicrobial patient specimen. Further optimization may furtherdecrease the total assay time and improve test performance.

Assay kinetics enabled sensitive, specific, and rapid detection of eachphenotype using a single challenge concentration of each antibiotic.

Example 8 Rapid Identification of Live Acinetobacter spp. inBronchoalveolar Lavage Specimens by Automated ImmunofluorescenceMicroscopy Introduction

Hospital acquired infections (HAI), and particularly nosocomialpneumonia, are leading causes of morbidity and mortality in criticallyill patients. Acinetobacter spp., including A. baumannii and severalother Acinetobacter genomospecies, are important pathogens in the ICU.

Hospital-adapted Acinetobacter harbors numerous antibiotic resistancemechanisms and presents serious diagnostic challenges. Because theseorganisms are often highly drug resistant, their identity and phenotypemarkedly influence the choice of therapy.

Culture-based systems are able to identify Acinetobacter spp. butrequire initial enrichment culturing and colony isolation. Culturingmethods therefore require as long as 48 hours for positiveidentification and antibiotic susceptibility testing. This is too longfor managing critical infectious diseases because initial therapy mustassure adequate control of disease progression.

Molecular methods shorten the identification, but cannot differentiatebetween live and dead, nor intact or fragmented bacteria, nor can theyquantify specimen contents. These are important criteria for many typesof specimen, particularly in diagnosing pneumonia.

In order to eliminate the delays required for culturing, it would bedesirable to analyze live organisms extracted directly from a patientspecimen. Such a method would require species identification andenumeration, as well as the ability to determine the viability ofindividual cells.

The purpose of this investigation was to characterize a method for rapididentification of Acinetobacter spp. extracted directly from a mockspecimen using fluorescent-labeled antibody paired with automated growthtracking of individual bacteria to determine viability. The experimentalmethods tested in this study are intended to become part of a new rapiddiagnostic system using bacteria extracted directly from a patientspecimen without prior enrichment culturing or colony isolation.

Materials and Methods

Acinetobacter spp. and non-Acinetobacter isolates were obtained fromATCC and JMI Laboratories (N. Liberty, Iowa). The collection included 19A. baumannii and 1 Acinetobacter genomospecies-13, plus 28non-Acinetobacter isolates of species often found in respiratoryspecimens.

Direct observation of bacteria was performed on a disposable fluidiccassette inserted into a custom bench-top instrument that combinesautomated digital microscopy, motion control, and analysis module.

The cassette contained multiple independent flowcells. Flowcells wereconstructed with transparent top and bottom surfaces to allow microscopeimaging. Each surface had a transparent electrode coating, forming anelectrophoresis chamber. The bottom surface was coated withpoly-L-lysine to immobilize bacteria upon surface contact.

Colonies from agar plates were resuspended in tryptic soy broth (TSB)and grown for 2 hours. Mock specimens were made by spiking log phasebacteria (approx. 5×10⁶ CFU/mL) into bronchoalveolar lavage (BAL) fluidfrom non-infected sheep. A specimen was then centrifuged on Percoll toreduce debris, washed and resuspended in electrokinetic capture buffer,and pipetted into a cassette's sample wells. Tests were also performedon isolates without BAL. For experiments on live/dead mixtures, liveorganisms were mixed with formalin-killed bacteria in a 1:1 ratio(McFarland standard).

Application of an electrical field caused bacteria to migrate to thepositively-charged lower electrode during a capture step. The bacteriaadhered to the surface coating, permitting subsequent medium exchanges.Photomicrographs illustrating microorganisms in a flowcell before andafter surface capture are shown in FIGS. 22A-22C. FIG. 22A illustratesunfocused images showing that bacteria are not in contact with thesurface. FIG. 22B illustrates bacteria and residual debris in focus onthe surface. FIG. 22C illustrates fluorescence image showingantibody-labeled bacteria. Each flowcell contained approximately 100 to500 founder cells on the surface within the digital microscope's 444×592um field of view. After capture, the flowcells were rinsed with TSB,removing electrokinetic concentration buffer.

Polyclonal antibodies were developed in chickens and isolated using acidprecipitation of yolk proteins followed by tangential flow filtrationusing a 100 kDa filter. Antibodies specific for Acinetobacter surfaceantigens were isolated from the yolk preparation by affinitypurification.

Antibody staining of immobilized bacteria was performed by incubation inthe affinity-purified IgY for 5 minutes in a 1% BSA/TSB stainingsolution. Primary antibody binding was followed by washing and detectionof bound IgY using 5-minute incubation in goat anti-chicken antibodyconjugated to Alexa-555. Quantitative image analysis computed the meanintensity of cell staining and the percentage of cells that stainedabove a threshold level criterion.

The instrument acquired time sequenced images for each of the flowcellsat 10-minute intervals. For growth measurement, the image analyzercomputed mass changes using dark field imaging mode. Clones wereconsidered to be growing if they exhibited at least 50% increase inintegrated intensity over the 40 minute growth period.

To test feasibility for polymicrobial multiplexing, 1:1 mixed species oflive Acinetobacter and Pseudomonas aeruginosa were spiked into BAL.Staining for P. aeruginosa used rabbit O-typing antisera and goatantirabbit antibody conjugated to Alexa-488.

Results

Anti-Acinetobacter antibody labeled 16 of 20 strains of Acinetobacterspp. and did not label 25 of 28 strains of non-Acinetobacter speciescommonly found in respiratory specimens (Table 15).

TABLE 15 Antibody staining results. Number Number Number PositiveNegative Species Tested (%) (%) A. baumannii 19 15 (79) 4 (21) Acinetobacter gsp. 13 1  1 (100) 0 (0)  Total 20 16 (80) 0 (0) Pseudomonas aeruginosa 7 0 (0) 7 (100) Stenotrophomonas maltophilia 4 0(0) 4 (100) Haemophilus influenzae 1 0 (0) 1 (100) Klebsiella pneumoniae4  1 (25) 3 (75)  Escherichia coli 3 0 (0) 3 (100) Enterobacteraerogenes 1 0 (0) 1 (100) Enterobacter cloacae 2  1 (50) 1 (50) Staphylococcus aureus 1 0 (0) 1 (100) Staphylococcus epidermidis 1 0 (0)1 (100) Staphylococcus haemolyticus 1 0 (0) 1 (100) Staphylococcuspneumoniae 1 0 (0) 1 (100) Staphylococcus pyogenes 1 0 (0) 1 (100)Staphylococcus salivarius 1 0 (0) 1 (100) Total 28  3 (11) 25 (89) 

Capture time was fixed at 300 seconds. Electrokinetic transport movedall bacteria above the capture area to the surface, determined byfocusing at different levels above the surface. Growth of immobilizedbacteria began after TSB wash without an appreciable lag time (<10min.).

Antibody did not detectably bind to BAL debris. Over 90% of live cellsextracted from the live control mock BAL specimen met the growthcriterion, indicating that sample preparation capture, and labeling didnot adversely affect viability. None of the spiked dead cells exhibitedgrowth. A mixture of live and formalin-killed cells resulted in stainingof both live and dead cells (FIGS. 23A-23C illustrate live andformalin-killed A. baumannii (ATCC 19606), partial field of view. FIG.23A illustrates phase contrast, with debris. FIG. 23B illustratesoutlines of image analyzer mapped contours of all presumptive cells.FIG. 23C illustrates fluorescence, antibody stained.

Growth measurement clearly differentiated between growing andnon-growing individual clones after approximately 30 minutes of growthmeasurement. In the mixed live/dead mock specimen, 33% of clones met theviability criterion. (FIGS. 24A-24C illustrates example of individualclones of ATCC 19606 Acinetobacter, live plus dead cells. Time sequence,dark field, partial field of view. FIG. 24A illustrates start ofinterval. FIG. 24B illustrates after 40 min. of growth. FIG. 24Cillustrates quantitation of individual clone growth from the images.

A mixture of live Acinetobacter and Pseudomonas exhibited the expectedstaining with respective antibodies. Of 344 total cells observed, 221stained with Acinetobacter antibody and 123 stained with P. aeruginosaantibody. FIGS. 25A-25C illustrate a mixture of species, A. baumanniiATCC 19606 and P. aeruginosa ATCC 35554, exhibiting the expectedstaining with respective antibodies (partial field of view). FIG. 25Aillustrates the mixed species in phase contrast. FIG. 25B illustratesstaining with anti-Acinetobacter. FIG. 25C illustrates staining withanti-Pseudomonas. None of the cells remained unstained, and none stainedwith both antibodies. Bacteria in control flowcells containing eachstrain alone were stained using their primary antibodies, and nocross-reactivity was observed for either one. Bacteria in separatecontrol flowcells containing each strain alone did not stain with eithersecondary antibody in the absence of primary antibody.

This set of conditions demonstrated the feasibility of concurrent colormultiplexing with multiple antibodies.

Conclusions

Polyclonal antibody developed against surface antigens of Acinetobacterspp. showed the potential for multiplexed identification in the presenceof interfering species commonly seen in respiratory specimens.Electrokinetic immobilization and species immuno-identification did notsignificantly affect cell viability. The experimental methods were ableto quantify the ratio of live cells in a mock specimen.Immuno-identification combined with automated growth) tracking ofimmobilized bacteria represents a rapid and potentially powerfulapproach to indentifying and differentiating intact live Acinetobacterspp. cells from dead or dormant cells directly from high-titerspecimens.

Example 9 Direct Identification of MRSA and MLSB Phenotypes inStaphylococcus aureus Using Small Numbers of Immobilized CellsIntroduction

Mechanisms of broad-spectrum resistance to B-lactam antibiotics presentserious clinical challenges, particularly with critically ill patients.Methicillin resistant S. aureus (MRSA) has become a major pathogenicphenotype that requires rapid identification in order to assure adequateinitial therapeutic coverage. MRSA is associated with multiple drugresistance mechanisms in addition to conferring total β-lactamresistance. Laboratories need new methods to rapidly determine all majorantibiotic resistance phenotypes. Conventional phenotyping methodsrequire growth of large numbers of bacteria, which lengthens the totaltime-to-result. New methods requiring small numbers of organisms fortesting could potentially obviate the need for overnight culturing andenable direct-from-specimen analysis.

Multiplexed direct cellular phenotyping offers a rapid alternativemethod, requiring relatively small numbers of cells. It has thepotential to overcome the inherent limitations of other rapid methods,such as gene-based detection, for which resistance expression lacks adirect molecular marker correlate. Direct cellular phenotyping showsevidence of meeting analytical challenges such as inducibility andheteroresistance that now complicate antibiotic susceptibility testing.

This study tested multiplexed assay methods intended to enable a newrapid diagnostic system that will use bacteria extracted directly from apatient specimen without prior enrichment or colony isolation. Thepurpose was to determine whether the novel direct cellular phenotypingmethods meet requirements for speed and accuracy in simultaneouslyidentifying two unrelated and clinically important resistance mechanismsin S. aureus using small numbers of bacterial cells.

Materials and Methods

Direct observation of bacterial response to antibiotic exposure wasperformed on a custom disposable 32-flowcell cassette (FIG. 4A) insertedinto an automated digital microscope with customized motion control andimage analysis software. Each flowcell (FIG. 4B) was independent.Flowcell top and bottom surfaces had transparent, electricallyconductive coatings for electrophoresis and microscopy. The bottomsurface was coated with poly-L-lysine that immobilized bacteria uponcontact.

A collection of oxacillin borderline-MIC isolates was provided by theCDC. The collection included 78 mecA-positive and 56 mecA-negativestrains, plus one strain with mutated mecA that produced a variant PBP2aprotein of unknown clinical significance. Tests also included CLSI QCstrains (data not shown), ATCC 43300 (MRSA), BAA-976 (macrolide efflux),BAA-977 (inducible MLSB phenotype), and 29213 (susceptible control). 44of the mecA-positive and 14 of the mecA-negative isolates were eitherconstitutively or inducibly resistant to clindamycin (CLI) according toD-test results. Table 16 lists the CLI resistance phenotype counts bymecA status.

TABLE 16 CLI resistance phenotype counts by mecA status. mecA MLS_(B)Phenotype Status D D+ HD R NEG S Total Positive 24 3 8 9 12 23 79Negative 10 2 1 1 3 39 56

Colonies from agar plates were resuspended in broth and grown for 2hours. Log phase S. aureus were resuspended in electrokinetic capturebuffer at 1×10⁶ CFU/mL. A 10 μL sample was pipetted into each flowcellof the cassette, and the cassette placed into the instrument.

Electrophoresis for 5 minutes concentrated bacteria to the flowcellsurface. Bacteria adhered to the capture coating, permitting subsequentmedium exchanges. Each 444×592 μm field of view contained approximately100-500 bacterial cells. All assays used Mueller-Hinton broth (MHB) as awash medium and reagent vehicle.

For each isolate, the system performed concurrent assays in separateflowcells: a growth control, a non-induction FOX test, a FOX-induced FOXtest, a non-induction CLI test, and an ERY-induced CLI test. Priorstudies had established 1 hr of 1 μg/mL FOX followed by 3 hrs of 6 μg/mLFOX as standard conditions. Other studies had established 1 hr of 0.1μg/mL ERY followed by 3 hrs of 0.5 μg/mL CLI as standard.

The instrument acquired images for each of the flowcells at 10-minuteintervals. The system performed growth rate measurements on the entirebacterial population within each field of view.

Prior studies established growth-rate interpretation criteria after thechallenge period. For MRSA identification, mecA-positive isolates hadgrowth rates greater than 0.1 divisions per hour (div/hr), andmecA-negative isolates had rates less than 0.1 div/hr. For MLSBidentification, CLI-resistant isolates had growth rates greater than 0.4div/hr and CLI-susceptible isolates had rates less than 0.4 div/hr.

Results

Growth began after bacterial immobilization and MHB wash without anappreciable lag time (<10 min)

78 of the 79 mecA-positive strains were classified as MRSA, and 56 ofthe 56 mecA-negative strains as MSSA (FIG. 26A). Negative growth ratesignifies cell lysis (loss of cell mass). As with all test methodsreported by the CDC (Swenson et al., 2007), the experimental methodclassified the mutated mecA strain (BS-089) as susceptible. MLSBidentification using ERY induction and CLI challenge (FIG. 26B; notedifference in Y-axis scale compared to FIG. 26A). This strain wasclassified MSSA in the tabulated results based on CLSI FOX-DD results.CLI-resistance was correctly characterized in 43 of the 44 mecA-positiveand 14 of the 14 mecA-negative isolates (FIG. 26B). The division ratefor the incorrectly classified strain is plotted separately as a stripedsquare (FIG. 26A) and a spotted square (FIG. 26B). Results were comparedto mecA PCR results, CLSI FOX disk diffusion (FOX-DD), and D-tests.

Discussion

The experimental method performed using a system and method inaccordance with various embodiments of the present disclosure met theobjectives of minimal starting cell count, rapid time to result, anddemonstrated accuracy comparable to that of FOX-DD and D-zone tests inidentifying the MRSA phenotype and CLI resistance in this oxacillin MICborderline collection.

Further optimization of the induction concentration and challengeconcentration may further decrease the total assay time using systemsand methods disclosed herein.

Conclusions

Analysis using a MADM system in accordance with various embodiments ofthe present disclosure required orders of magnitude fewer cells(100-500) and a dramatically decreased period of time (4 hrs) for MRSAand MLSB identification as compared to the number of cells (approx.10⁴-10⁵) and length of time (days) required by conventionalmicrobiological methods. If combined with compatible concentration andin situ identification methods, the rapid direct phenotyping methodenabled the system and methods of the present disclosure has thepotential to eliminate the need for overnight culturing and colonyisolation with patient specimens such as bronchoalveolar lavage fluid,wound swabs, and other high-titer specimens. The analytical speed of theautomated system was consistent with that required to guide initialempiric therapy in critically ill patients.

Example 10 Detection of Persister Clones Using Vital/Mortal Staining

Current microbiological methods use the absence or presence of bacterialcell growth and/or division to determine the effects of antibiotics onbacteria. Unfortunately, standard clinical microbiology approaches todetermination of the minimum inhibitory concentration (MIC) of anantibiotic such as those provided by CLSI and EUCAST do not account forslowly growing clones or for clones that may grow after a dormantperiod. Survival of these persister clones may have dire consequencesfor a patient if ignored during the selection of an antibiotic treatmentschema. Systems and methods in accordance with various embodiments ofthe present disclosure are capable of detecting not only microorganismgrowth and/or division, but can also detect several other indicators ofbacterial activity, enabling a more thorough characterization ofpersister clones during antibiotic susceptibility testing.

In order to identify persister clones capable of retaining activityafter exposure to antibiotics, a variety of indicators that revealeddifferent physiological states were tested (Table 17). Physiologicindicators of active bacteria include indicators of growth,responsiveness to external stimulus, transcription, translation, energydependent activity, enzyme activity, and an intact permeability barrier.The indicators tested experimentally were grouped according to thefollowing functional categories of physiologic states to which they areresponsive, which included indicators of respiratory and metabolicactivity, and membrane integrity.

TABLE 17 Indicators of bacterial activity grouped by functional categoryMembrane Potential DiOC₂(3) DiBAC₄(3) Respiratory/Metabolic Activity CTCC₁₂-Resazurin CFDA CFDA, SE 2-NBDG Membrane Integrity YO-PRO-1 PIPlasmolysis

Results Membrane Potential Indicators

Active bacterial cells maintain a proton gradient across their cellmembranes, creating a membrane potential that may be measured usingfluorescent dyes. However, the dyes used in this study to evaluatemembrane potential are toxic to bacteria and can only be used as anendpoint assay.

DiBAC₄(3) (Molecular Probes) is an anionic dye that enters depolarizedcells (i.e., cells lacking a membrane potential) and fluoresces in thered channel when it binds to intracellular proteins or membranes. Usingthis indicator, 18.5% of an overnight refrigerator stock (control) and95% of heat-killed E. coli stained with 10 g/ml DiBAC₄(3) after a 15 minincubation period at room temp.

DiOC₂(3) (Molecular Probes) is a dye that fluoresces in the red channelwhen it is highly concentrated and self-associates in cells with anintact membrane potential. Fewer DiOC₂(3) molecules are able to enterinactive cells and will exhibit a green fluorescence as a singlemolecule. The red:green fluorescence ratio is used to normalize thefluorescence results of cells with differing sizes.Carbonylcyanide-m-chlorophenylhydrazone (CCCP) is a proton ionophorethat disrupts the proton gradient and was used to treat E. coli cells inan experimental sample that was compared to an untreated overnightrefrigerator stock. No significant difference in red:green fluorescenceratios were observed between the O/N refrigerator stock and CCCP-treatedE. coli, and further experimentation would be required to determinewhether different dye concentrations would be effective for evaluationof cells with intact versus depolarized cell membranes using the systemof the present disclosure.

Respiratory/Metabolic Activity Indicators

CTC (5-Cyano-2,3-ditolyl tetrazolium chloride) (Sigma) is a redox dyethat produces an insoluble fluorescent formazan when it is reduced.Metabolically active bacteria that reduce CTC retain the formazanintracellularly and are detectable based on the fluorescence of theformazan. Overnight refrigerator stock cultures of both E. coli and S.aureus were fluorescently labeled with the reduced form of CTC after 25min at 35° C. with 4 mM CTC in 0.9% NaCl.

2-NBDG (Molecular Probes) is a fluorescent derivative (green channel) ofD-glucose that is internalized by active bacteria. Overnightrefrigerator stock cultures of E. coli and K. pneumoniae werefluorescently labeled after approximately 1 min of exposure to 1 μM2-NBDG. However, overnight refrigerator cultures of H. influenzae, S.maltophilia, and S. aureus did not fluoresce despite independentverification of the viability of the stock cultures used for) the 2-NBDGexperiment. While S. maltophilia does not metabolize glucose and servedas a negative control providing results that conformed withexpectations, the failure of H. influenza and S. aureus indicates thatfurther experimentation is required and other carbon sources may providebetter results and/or compatibility with a wider array of microbialspecies.

C12-Resazurin (Molecular Probes) is a redox dye that is reduced to ared-fluorescent C12-resorufin by metabolically active bacteria. The dyesis non-toxic and stable in culture media according to manufacturerliterature. Overnight refrigerator stock cultures of S. aureus were ableto reduce C12-Resazurin based on detection of the red-channelfluorescence. Unexpectedly, both heat-killed (2-7 hr post heat-kill) andisopropyl alcohol killed S. aureus were also fluorescently labeled withC12-Resazurin. Further experimentation would be required to evaluate thesuitability of this dye for detection of metabolically active bacteriawhile reducing false positive results.

Carboxyfluorescein diacetate/carboxyfluorescein diacetate succinimidylester (CFDA/CFDA-SE; Molecular Probes) compounds are converted intoamine-reactive fluorescent molecules when cleaved by esterases presentwithin active cells. When CFDA-SE is taken up by live cells, thefluorescent molecule produced by esterase cleavage and amine reaction isretained inside the cell. Hence, if the cell eventually becomesinactive, these cells will continue to fluoresce. The product formed byuptake and processing of CFDA, on the other hand, should be more “leaky”over time and may exit a dead cell. The difference in kinetics of dyeexit between an active vs. inactive cell is unknown. Therefore, althoughthe dyes are non-toxic, they would likely be most effective as anendpoint assay. In experiments performed using the MADM system,heat-killed cells and isopropyl alcohol killed cells demonstratedfluorescence when dyes were added immediately after treatment. A twohour post-heat-kill delay was sufficient to eliminate the possibleresidual esterase activity observed according to CFDA-SE assays (i.e.,no fluorescence was observed with a delayed assay following heat-kill).Shorter delay times have not yet been investigated but may be compatiblewith avoiding false positive results while providing a shorter assayperiod.

Membrane Integrity Indicators

YO-PRO-1 (Molecular Probes) is a DNA-intercalcater that penetratesdamaged membranes but not intact membranes. Experiments were conductedto verify non-toxicity of the dye to various test microorganisms,including E. coli, S. aureus, and H. influenza. This indicator has beensuccessfully used as an indicator to assay membrane permeability in theaforementioned species, and the use of YO-PRO-1 to evaluate antibioticsusceptibility of individual microorganisms in a collection of largenumbers of microorganisms is described in detail in U.S. Pat. No.7,341,841.

Propidium iodide (PI; Sigma) is another red fluorescentDNA-intercalcater that penetrates damaged membranes but not intactmembranes. Initial studies using PI were very promising and appearedcomparable to YO-PRO-1. However, fluorescent signal intensity vs.background was not as high as for YO-PRO-1.

Plasmolysis is a method of detecting membrane integrity by exposingcells to a very hypertonic solution, such as may be performed bysubjecting a cell to osmotic pressure by manipulating the concentrationof a solute, for example, sodium chloride. Active cells will shrink insize in response to the osmotic pressure while inactive cells are unableto respond and will remain the same size. The ability to evaluateplasmolysis to assess cell membrane integrity was tested in E. coli,with 77% of cells from overnight refrigerator stock cultures decreasingin size when exposed to 0.9% NaCl, whereas 90% of heat killed cells didnot exhibit a response. While certain reports suggest that plasmolysismay be more difficult to detect in gram-negative bacteria than ingram-positive cells, our results in experiments with E. coli indicatethat plasmolysis may observed in this gram-negative organism using thesystem of the present disclosure.

Conclusion

Various dyes were successfully used and are compatible with the systemand methods disclosed herein as indicators of cell viability that can beadded at the outset of a growth determination evaluation (i.e.,non-toxic cell membrane permeability indicators such as YO-PRO-1), whileothers can be used as endpoint indicators of cell activity or viabilityfollowing growth evaluation and the results of such assays may beoverlaid or correlated with growth determination conclusions generatedusing the growth analysis module as described herein.

Example 11 Microorganism Object Model Fitting

In accordance with various embodiments, data analysis may comprisefitting detected sample elements or objects to models of microorganismshapes. Individual cellular object candidates are generally of circular(i.e., spherical in three dimensions) and/or rod-like (i.e., ellipsoidin three dimensions) shapes. Once clones start growing, the signal shapeand signal intensity of each microorganism object or cell is assumed tobe a superposition of a number of basic shapes or shape models to whichdetected image shapes may be compared in a model fitting processdescribed in greater detail in the present example. The model fittingprocess may be applied to each detected microorganism or cellular objectcandidate. Such an approach allows for detection robust to noise andneighboring objects as well as artifact rejection. Goodness-of-fitmetrics are applied to discard microorganism objects that do not fitmicroorganism shape models within specified parameters, i.e., detectedobjects that do not “obey” the models are rejected. This approach alsoallows for effective signal intensity estimation of the clones (i.e.,clone intensity), which is the ultimate goal of the processing pipeline.

The present example describes application of microorganism object modelfitting to image data. However, data obtained using a variety of otherdetection systems and/or methods may be processed as described below. Asused in the present example, the term “cumulative image” refers to aper-pixel aggregate of registered changed in stack images over time.Similarly, the term “cumulative background image” refers to a per-pixelaggregate of registered changes in background images over time.

Registration information derived from a registration process performedby the analysis module, such as the registration procedure described inExample 12, is used to align both signal and background images fromconsecutive frames. In order to constrain the model fitting procedure toareas of the image that are changing (i.e., to focus the analysis onlyon growing clones), a mask of significant change in signal-to-backgroundrelationship in the vicinity of the detections is constructed accordingto the following functions:

${{Track}_{Mask}\left( {i,j} \right)} = \left\{ {\begin{matrix}{1,} & {{{if}\mspace{14mu} \frac{\sum_{m,n}{{CumI}_{k}\left( {{{{Seed}(k)} + m},{{{Seed}(k)} + n}} \right)}}{\sum_{m,n}{{{BG}_{k}\left( {{{{Seed}(k)} + m},{{{Seed}(k)} + n}} \right)}/N}}} > 40} \\{- 1} & {{{if}\mspace{14mu} {\sum\limits_{m,n}{{\begin{matrix}{{{BG}_{k}\left( {{{{Seed}(k)} + m},{{{Seed}(k)} + n}} \right)} -} \\{{BG}_{k - 1}\left( {{{{Seed}(k)} + m},{{{Seed}(k)} + n}} \right)}\end{matrix}}/N}}} > {\max \; {BgChange}}} \\{0,} & {otherwise}\end{matrix},} \right.$

where (m, n) are dimensions of rectangles bounding seed detections,N=m*n,

maxBgChange=max(CumBg _(k−1))

and

CumI _(k) =I _(K−1)+(register(I _(K),offset_(K))−I _(K−1))

CumBG _(k) =BG _(K−1)+(register(BG _(K),offset_(K))−BG _(K−1)).

Registered versions of both cumulative and background images for fittingmodels to I_(K) are generated according to the following functions:

CumI ^(R) _(K)=register(CumI _(K),offset_(K))

BG ^(R) _(K)=register(BG _(K),offset_(K))

Before model fit is tested, the input stack image I_(K) is preprocessedto remove hot pixels which can skew the intensity profiles of smallcells in a manner similar to that performed during a seeding process,described elsewhere herein. The result of preprocessing is image I′_(K).

The direction of alignment may be determined for signal and backgroundimages in accordance with the following process. A predefined grid of5×5 pixels with intensities centered on each of the Seed(k) locationsmay be fitted with a second order surface of the form M(x,y)=a²x²+b²y²+cxy+d, illustrated in FIG. 27A.

A least squares fit may be found by solving for the four coefficients(Levenberg-Marquard) for all pixels on the 5×5 grid simultaneously ifthey are within Track_(Mask)(i,j).

The following vector of eigenvalues comprises parameters of the modelingellipse and includes the semi-minor axis, the semi-major axis, thetangent of the orientation angle, and height:

$E_{Fit} = {{eig}\left( \begin{bmatrix}{2a} & c \\c & {2b}\end{bmatrix} \right)}$

A model mask (binary) Model_(Mask) is the generated based on thedimensions of the fitted model:

modelWidth=2*(longAxis+5)+1

modelHeight=modelWidth

Values are assigned according to the following function:

${{Model}_{Mask}\left( {i,j} \right)} = \left\{ \begin{matrix}{1,} & {{if}\mspace{14mu} {inside}\mspace{14mu} {ellipse}\mspace{14mu} \left( {{{major}\; {Axis}},{minorAxis},\; \theta} \right)} \\{0,} & {otherwise}\end{matrix} \right.$

The model mask is smoothed with a 7×7 Gaussian kernel and updated asfollows:

${{Model}_{Mask}^{\; \prime}\left( {i,j} \right)} = \left\{ \begin{matrix}{10,} & {{{if}\mspace{14mu} {{Model}_{Mask}\left( {i,j} \right)}} > 0} \\\begin{matrix}{{{{Model}_{Mask}\left( {i,j} \right)} \otimes K} +} \\{{{Model}_{Mask}\left( {i,j} \right)},}\end{matrix} & {otherwise}\end{matrix} \right.$

The model is retained if certain constraints are satisfied, such asgoodness-of-fit criteria set forth by the following function:

${{majorAxis} < 30},{{area} < 500},{\frac{minorAxis}{majorAxisM} < {0.5.}}$

Each model is evaluated based on the underlying image data and thefollowing parameters are computed:

${modelProbablity} = \frac{\begin{matrix}{{\sum_{m,n}{{CumI}_{K}^{R}\left( {{i + m},{j + n}} \right)}} -} \\{{{BG}_{K}^{R}\left( {{i + m},{j + n}} \right)} - {{BG}_{K - 1}^{R}\left( {{i + m},{j + n}} \right)}}\end{matrix}}{\left( {N*10} \right)}$${modealBackground} = \frac{\sum_{m,n}{{BG}_{K - 1}^{R}\left( {{i + m},{j + n}} \right)}}{N}$

for i,j such that Model′_(Mask) (i,j)>0. N is number if pixels in modelmask.

modelIntensity=d−(1+(1.1−1)*10)*modelBackground.

The aggregate error of a set of overlapping models is computed byintegrating over differences of the actual values under models and thefitted errorFit.

As a result of the model parameter estimation, outliers can bediscarded.

If errorFit>300 or errorFit<−100, the model is considered invalid. Also,a model may be considered invalid if its dimensions are excessivelylarge, such as:

majorAxis>8, minorAxis>6.5.

The background image may be updated by incorporating the per-pixelbackground values for the newly estimated models.

The cumulative background image may also be updated based on informationfrom the newly estimated models.

Example 12 Image Content Registration Procedure

In accordance with various embodiments, time-lapse imagery of the samelocation within a channel is acquired by using the same commandedlocation of the stage apparatus. This process may be subject toinaccuracies due to stage imperfections and possible movement of thesample cassette relative to the stage. It is important to align imagesfrom the stack to the same sampling grid since tracking growth of aclone becomes greatly simplified. Also, as a result of registration,per-pixel signal and noise statistics over the full stack becomesaccessible.

In accordance with various embodiments, the following procedure isapplied to every consecutive pair of images in the time-lapse stack.

Since permanent fiducial marks may not be available in any of the imagesin the stack, the frame content of two consecutive frames −I_(k) andI_(k+1), may be used for alignment. It should be noted that theregistration module disclosed in the present example implements 2-Dtranslational registration only; however, in accordance with variousembodiments, three-dimensional translational registration is alsopossible and within the scope of the present disclosure. The followingprocedure is employed to estimate the content to be used in theregistration process:

First, a starting subregion R of image dimension for content estimationis identified as the center of the image:

${Width}_{R} = {2*\frac{\min \left( {{{height}\left( I_{k} \right)},{{width}\left( I_{k} \right)}} \right)}{0.25}}$Height_(R) = Width_(R)${Center}_{R} = \frac{\min \left( {{{height}\left( I_{k} \right)},{{width}\left( I_{k} \right)}} \right)}{0.5}$

The center of R is then refined by placing it at the location of themost significant coefficient within the central region:

Center_(R)=argmax_([i,j]) P(i,j), for all i,jεR.

The region is then convolved with an edge-finding high pass filterconsisting of two 7×7 kernels—H_(vertical), H_(horizontal)—for bothI_(k) and I_(k+1) as follows:

R _(Kedge) =R _(K)

H _(vertical) +R _(K)

H _(horizontal),

retaining non-zero values only for those pixels that have not beenidentified as background according to the following function:

${R_{Kedge}\left( {i,j} \right)} = \left\{ {\begin{matrix}{0,} & {{{if}\mspace{14mu} {R_{Kedge}\left( {i,j} \right)}} > {{BG}_{score}\left( {i,j} \right)}} \\{{R_{Kedge}\left( {i,j} \right)},} & {otherwise}\end{matrix}.} \right.$

The above procedure is repeated for I_(k+1) to obtain R_(K+1,edge),except no restrictions are placed on including background pixels. Both“feature” images are then reduced in size by a factor of 4 and smoothedwith a Gaussian kernel:

R _(K,feature) =N(0,0.0002)

resize(R _(Kedge),0.25)

R _(K+1,feature) =N(0,0.0002)

resize(R _(K+1,edge),0.25)

Smoothed statistics on feature region R_(K,feature) are then computed asfollows:

${{R_{K,{VAR}}\left( {i,j} \right)} = \sqrt{\frac{\begin{matrix}{{\sum_{m,n}{\sum_{m,n}{R_{K,{feature}}\left( {{i + m},{j + n}} \right)}^{2}}} -} \\{\left( {\sum_{m,n}{\sum_{m,n}{R_{K,{feature}}\left( {{i + m},{j + n}} \right)}}} \right)^{2}/N^{2}}\end{matrix}}{R_{K,{feature}}^{2} - 1}}},$

where N is the number of pixels traversed with m={−10,10}, n={−10,10}.

Values of R_(K,VAR)(i,j) are enforced to be positive:

${R_{K,{VAR}}\left( {i,j} \right)} = \left\{ \begin{matrix}{{R_{K,{VAR}}\left( {i,j} \right)},} & {{{if}\mspace{14mu} {R_{K,{VAR}}\left( {i,j} \right)}} \geq 0} \\{0,} & {otherwise}\end{matrix} \right.$

Smoothing of the variance estimate above is performed as follows:

${{R_{K,{VAR\_ SMOOTH}}\left( {i,j} \right)} = \frac{\sum_{m,n}{\sum_{m,n}{R_{KVAR}\left( {{i + m},{j + n}} \right)}}}{N^{2}}},$

where N is the number of pixels traversed with m=−{−20,20}, n={−20,20}.

An offset between two frames I_(k) and I_(k+1) is computed by shiftingR_(K+1,feature) against R_(K,feature) with a predetermined step andevaluating the difference between the two. The offset resulting in theminimum difference is the final registration solution, solved accordingto the following functions:

${offset}_{i} = {\arg \; {\min_{\lbrack{i,j}\rbrack}\frac{{\sum_{ɛ}{R_{K,{feature}}\left( {i,j} \right)}} - {R_{{K + 1},{feature}}\left( {{i + ɛ},{j + ɛ}} \right)}}{N}}}$iterate  while (i < max  Step):${{offset}_{i + 1} = {\arg \; {\min_{\lbrack{i,j}\rbrack}\frac{\begin{matrix}{{\sum_{ɛ}{R_{K,{feature}}\left( {i + {{offset}_{i,}j} + {offset}_{i}} \right)}} -} \\{R_{{K + 1},{feature}}\left( {{i + {offset}_{i} + ɛ},{j + {offset}_{i} + ɛ}} \right)}\end{matrix}}{N}}}},$

where={−1,1}, N=9, and number of iterations maxStep=15. It should benoted that the difference is evaluated only for those locations (i,j)that represent significant variance, such that:

R _(K,VAR) _(SMOOTH) (i,j)>mean(R _(K,VAR) _(SMOOTH) )+6*std(R _(K,VAR)_(SMOOTH) )

Final registration offset for images I_(k) and I_(k+1) is thenRegOffset_(K,K+1)=offset upon convergence on the minimum differenceaccording to the above process.

Example 13 Identification and Growth Rate Quantification of IndividualPhysically Isolated Bacterial Clones Using Impedance Measurements

An impedance-based quantitative growth measurement system is used tomeasure the growth of physically isolated individual microorganisms innear real time. The system provides a rapid and accurate evaluation ofthe growth dynamics for the population of viable organisms in thesample. The present example does not rely on a computer-based system tolocate, model, and track discrete microorganisms, but instead monitorsdiscrete impedance sensors for changes in measured value over time thatmay correspond to growth of a microorganism.

The system disclosed herein may be a bench top instrument that combinesa disposable microfluidic cartridge comprising a plurality of individualwells, each having an integrated impedance detector. The number of wellsis in large excess of the number of bacteria present in the sample suchthat either through the expected low concentration level or throughdilution, the likelihood or probability is such that a singlemicroorganism or no microorganisms are present in a single well. Anexample of a 4×4 array of wells is illustrated in FIGS. 28A-28C. Imagesof the clones in each well may be taken at several time points (e.g., 0min (FIG. 28A, 90 min (FIG. 28B, and 180 min (FIG. 28C)) over the courseof data acquisition by the impedance microorganism detection system,such as for purposes of quality control to confirm presence ofindividuated clones in each well. The dilution fluid can contain growthmedia, or the well fluid can be exchanged with growth media. Impedancesignals obtained are associated with viable cells (dead cells haveessentially zero impedance) and the change of impedance over time ismonitored. Prospective impedance data measured for each well at 10minute intervals is summarized in Table 18. A prospective example ofimpedance values measured over time for 16 clones is illustrated in FIG.29.

TABLE 18 Impedance measurements of individual wells over time for 16clones.* Note: signal associated with non-viable cell like material inwell 16 (lower right corner) does not contribute to impedance signal andimpact the mass measurement. Well Number Time 1 2 3 4 5 6 7 8 0 41.29643.584 43.641 46.457 47.165 41.471 48.518 43.278 10 42.503 44.687 44.98947.779 48.380 42.656 49.933 44.759 20 43.811 46.047 46.066 49.031 49.87144.026 51.455 46.570 30 45.397 47.473 47.717 50.543 51.893 45.499 53.02148.126 40 46.547 48.848 48.854 52.219 53.222 46.777 55.039 50.154 5048.296 50.277 50.119 53.470 54.888 48.188 56.280 52.047 60 49.468 51.61051.579 55.001 56.807 49.360 57.854 54.545 70 51.043 52.708 52.581 56.16759.211 50.763 59.290 57.397 80 52.698 53.881 53.962 58.561 62.212 51.83060.897 61.250 90 55.270 55.935 55.240 61.212 66.661 53.377 63.898 62.586100 58.209 57.726 56.643 63.754 69.513 55.424 65.858 65.458 110 60.97860.384 59.340 67.519 76.605 57.384 68.558 70.603 120 63.499 63.50962.165 72.725 80.176 59.498 71.771 74.628 130 67.458 67.789 65.88376.138 87.168 62.219 75.743 82.903 140 73.840 72.558 70.576 83.38094.533 66.140 80.863 94.734 150 80.376 78.081 78.122 92.833 108.40771.300 84.401 105.527 160 91.172 86.028 84.081 101.488 128.872 77.71091.934 120.859 170 105.529 94.013 93.765 115.226 153.921 84.046 101.049135.148 180 120.720 105.072 103.853 131.973 180.348 96.090 111.628159.500 Well Number Time 9 10 11 12 13 14 15 16 0 40.735 46.221 47.11530.629 40.745 37.947 41.662 40.283 10 42.009 47.399 48.536 31.702 41.89939.622 43.038 41.689 20 43.176 48.735 49.870 33.316 43.248 41.271 44.50143.269 30 44.838 50.390 51.729 35.193 44.623 43.186 46.590 44.930 4045.877 51.864 53.104 37.039 46.081 44.910 48.444 46.322 50 47.248 53.21254.557 39.163 47.648 46.516 50.001 47.943 60 48.691 54.510 56.202 41.83749.647 48.238 51.714 49.203 70 49.797 55.881 57.849 46.120 51.265 50.03454.241 50.659 80 50.994 57.098 59.442 49.031 52.869 52.483 58.590 52.03090 52.617 58.616 60.630 51.357 55.067 54.156 62.421 53.626 100 54.07260.203 62.325 52.892 58.380 57.673 65.487 55.612 110 56.815 62.24865.121 55.895 61.666 61.312 69.916 57.746 120 58.101 64.876 68.97062.947 64.408 66.454 75.207 60.100 130 61.020 68.295 73.035 68.80672.993 66.806 84.316 62.839 140 65.929 72.874 79.439 81.331 76.09373.300 91.680 67.579 150 71.410 77.531 85.882 88.580 82.593 76.243102.863 73.387 77.193 84.456 96.801 110.156 94.276 84.642 117.552 80.22984.976 95.380 108.161 128.724 106.354 91.331 139.691 86.143 97.863111.551 120.472 160.398 122.711 107.055 163.237 97.697 *Note: Well datatheoretical only. Actual well data may vary.

The impedance values are logarithm transformed and fitted with a cubicpolynomial, then the population of clones are converted into a measureof growth and/or susceptibility as described elsewhere herein.

Example 14 Detection, Identification, and Growth Rate Quantification ofIndividual Bacterial Clones Using Bioelectroanalysis

A sample comprising microorganisms is loaded into a system comprising adisposable microfluidic cartridge with a surface having a detectionsurface with an array of discrete, individually addressedmicroelectrodes suitable for performing impedance measurements.Microorganisms introduced into the cartridge are electrokineticallyconcentrated to the detection surface of the cartridge prior toperforming bioelectroanalysis. Growth medium compatible withbioelectroanalysis is introduced to the chamber. The array ofmicroelectrodes is used to measure cell wall charges and the release ofions and other osmolytes from microorganisms concentrated on thecartridge surface comprising the microelectrodes. A series of impedancemeasurements are recorded at each electrode in the array over time andsignal analysis and detection is performed using analysis module 140 asdescribed elsewhere herein. Impedance values measured over time for eachdiscrete electrode in the array may be plotted, and impedance signalintensity data may resemble pixel intensity data acquired using variousoptical-based methods. Exemplary data is provided in FIGS. 30A-30D and31A-31D. Likewise, FIG. 32 illustrates impedance signal intensity valuesthat may be obtained for microorganisms associated with a detectionsurface comprising a microelectrode array, with each square column inthe figure corresponding to impedance signal from a sensor in the array.Background and noise in the impedance signal intensities is evaluatedand used for clone detection, model fitting, and growth likelihoodanalysis as described in detail herein with respect to image data.

FIGS. 30A-30D illustrate prospective composite graphical representationsof impedance data acquired at four time points for 64 clones identifiedusing the systems and methods of the present disclosure. FIGS. 31A-31Dillustrate prospective composite thumbnails images of darkfieldphotomicrographs of the same individuated clones at the same timepoints. Image data and impedance data are strongly correlated, andeither may be used to derive quantitative numerical data suitable fordetermining growth, such as prospective data presented in a table formatshown in Table 17.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the present disclosurewithout departing from the spirit or scope of the disclosure. Thus, itis intended that the present disclosure cover the modifications andvariations of this disclosure provided they come within the scope of theappended claims and their equivalents.

Any communication, transmission and/or channel discussed herein mayinclude any system or method for delivering content (e.g. signals,images, data, information, metadata, etc), and/or the content itself.The content may be presented in any form or medium, and in variousembodiments, the content may be delivered electronically and/or capableof being presented electronically.

In various embodiments, the methods described herein are implementedusing the various particular machines described herein. The methodsdescribed herein may be implemented using those particular machinesdescribed herein, and those hereinafter developed, in any suitablecombination, as would be appreciated immediately by one skilled in theart. Further, as is unambiguous from this disclosure, the methodsdescribed herein may result in various transformations of certainarticles.

For the sake of brevity, conventional data networking, applicationdevelopment and other functional aspects of the systems (and componentsof the individual operating components of the systems) may not bedescribed in detail herein. Furthermore, the connecting lines shown inthe various figures contained herein are intended to represent exemplaryfunctional relationships and/or physical couplings between the variouselements. It should be noted that many alternative or additionalfunctional relationships or physical connections may be present in apractical system.

The various system components discussed herein may include one or moreof the following: a host server or other computing systems including aprocessor for processing digital data; a memory coupled to the processorfor storing digital data; an input digitizer coupled to the processorfor inputting digital data; an application program stored in the memoryand accessible by the processor for directing processing of digital databy the processor; a display device coupled to the processor and memoryfor displaying information derived from digital data processed by theprocessor; and a plurality of databases. Various databases used hereinmay include: client data; merchant data; financial institution data;and/or like data useful in the operation of the system. As those skilledin the art will appreciate, user computer may include an operatingsystem (e.g., a Windows, UNIX, Linux, Solaris, Mac OS, or other suitableoperating system) as well as various conventional support software anddrivers typically associated with computers.

The present system or any part(s) or function(s) thereof may beimplemented using hardware, software or a combination thereof and may beimplemented in one or more computer systems or other processing systems.However, the manipulations performed by embodiments were often referredto in terms, such as matching or selecting, which are commonlyassociated with mental operations performed by a human operator. No suchcapability of a human operator is necessary, or desirable in most cases,in any of the operations described herein. Rather, the operations may bemachine operations. Useful machines for performing the variousembodiments include general purpose digital computers or similardevices.

In fact, in various embodiments, the embodiments are directed toward oneor more computer-based systems capable of carrying out the functionalitydescribed herein. The computer-based system includes one or morecomputers and/or one or more processors. The processor is connected to acommunication infrastructure (e.g., a communications bus, cross overbar, or network). Various software embodiments are described in terms ofthis exemplary computer system. A computer system can include a displayinterface that forwards graphics, text, and other data from thecommunication infrastructure (or from a frame buffer not shown) fordisplay on a display unit.

A computer system also includes a main memory, such as for examplerandom access memory (RAM), and may also include a secondary memory. Thesecondary memory may include, for example, a hard disk drive and/or aremovable storage drive. The removable storage drive reads from and/orwrites to a removable storage unit in any suitable manner. As will beappreciated, the removable storage unit includes a computer usablestorage medium having stored therein computer software and/or data.

In various embodiments, secondary memory may include other similardevices for allowing computer programs or other instructions to beloaded into a computer system. Such devices may include, for example, aremovable storage unit and an interface.

A computer system may also include a communications interface.Communications interface allows software and data to be transferredbetween a computer system and external devices. Software and datatransferred via communications interface are in the form of signalswhich may be electronic, electromagnetic, optical and/or other signalscapable of being received by communications interface. These signals areprovided to communications interface via a communications path (e.g.,channel).

The terms “computer program medium” and “computer usable medium” and“computer readable medium” are used to generally refer to media such asremovable storage drive and a hard disk installed in hard disk drive.These computer program products provide software to a computer system.

Computer programs (also referred to as computer control logic) arestored in main memory and/or secondary memory. Computer programs mayalso be received via communications interface. Such computer programs,when executed, enable the computer-based system to perform the featuresas discussed herein. In particular, the computer programs, whenexecuted, enable the processor to perform the features of variousembodiments. Accordingly, such computer programs represent controllersof the computer-based system.

In various embodiments, software may be stored in a computer programproduct and loaded into computer system using removable storage drive,hard disk drive or communications interface. The software, when executedby the processor, causes the processor to perform the functions ofvarious embodiments as described herein. In various embodiments,hardware components such as application specific integrated circuits(ASICs). Implementation of the hardware state machine so as to performthe functions described herein will be apparent to persons skilled inthe relevant art(s).

A web client includes any device (e.g., personal computer) whichcommunicates via any network, for example such as those discussedherein. Such browser applications comprise Internet browsing softwareinstalled within a computing unit or a system to conduct onlinetransactions and/or communications. These computing units or systems maytake the form of a computer or set of computers, although other types ofcomputing units or systems may be used, including laptops, notebooks,tablets, hand held computers (e.g., smartphones), set-top boxes,workstations, computer-servers, main frame computers, mini-computers, PCservers, pervasive computers, network sets of computers, personalcomputers.

In various embodiments, a web client may or may not be in direct contactwith an application server. For example, a web client may access theservices of an application server through another server and/or hardwarecomponent, which may have a direct or indirect connection to an Internetserver. For example, a web client may communicate with an applicationserver via a load balancer. In an exemplary embodiment, access isthrough a network or the Internet through a commercially-availableweb-browser software package.

In various embodiments, components, modules, and/or engines of system100 (e.g., Healthcare IS 150) may be implemented as micro-applicationsor micro-apps. Micro-apps are typically deployed in the context of amobile operating system. The micro-app may be configured to leverage theresources of the larger operating system and associated hardware via aset of predetermined rules which govern the operations of variousoperating systems and hardware resources. For example, where a micro-appdesires to communicate with a device or network other than the mobiledevice or mobile operating system, the micro-app may leverage thecommunication protocol of the operating system and associated devicehardware under the predetermined rules of the mobile operating system.Moreover, where the micro-app desires an input from a user, themicro-app may be configured to request a response from the operatingsystem which monitors various hardware components and then communicatesa detected input from the hardware to the micro-app.

As used herein, the term “network” includes any cloud, cloud computingsystem or electronic communications system or method which incorporateshardware and/or software components. Communication among the parties maybe accomplished through any suitable communication channels, such as,for example, a telephone network, data network, Internet, point ofinteraction device, online communications, satellite communications,off-line communications, wireless communications, transpondercommunications, local area network (LAN), wide area network (WAN),virtual private network (VPN), networked or linked devices, and/or anysuitable communication or data input modality.

The various system components may be independently, separately orcollectively suitably coupled to the network via data links whichincludes, for example, a connection to an Internet Service Provider(ISP) over the local loop as is typically used in connection withstandard modem communication, cable modem, Dish networks, ISDN, DigitalSubscriber Line (DSL), or various wireless communication methods.

“Cloud” or “Cloud computing” includes a model for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, servers, storage, applications, and services)that can be rapidly provisioned and released with minimal managementeffort or service provider interaction. Cloud computing may includelocation-independent computing, whereby shared servers provideresources, software, and data to computers and other devices on demand.

As used herein, “transmit” may include sending electronic data from onesystem component to another over a network connection. Additionally, asused herein, “data” may include encompassing information such ascommands, queries, files, data for storage, and the like in digital orany other form.

The system contemplates uses in association with web services, utilitycomputing, pervasive and individualized computing, security and identitysolutions, autonomic computing, cloud computing, commodity computing,mobility and wireless solutions, open source, biometrics, grid computingand/or mesh computing.

Any databases discussed herein may include relational, hierarchical,graphical, or object-oriented structure and/or any other databaseconfigurations. Moreover, the databases may be organized in any suitablemanner, for example, as data tables or lookup tables. Each record may bea single file, a series of files, a linked series of data fields or anyother data structure. Association of certain data may be accomplishedthrough any desired data association technique such as those known orpracticed in the art.

The data set annotation may be used for other types of statusinformation as well as various other purposes. For example, the data setannotation may include security information establishing access levels.The access levels may, for example, be configured to permit only certainindividuals, levels of employees, companies, or other entities to accessdata sets, or to permit access to specific data sets based on thetransaction, merchant, issuer, user or the like. Furthermore, thesecurity information may restrict/permit only certain actions such asaccessing, modifying, and/or deleting data sets. In one example, thedata set annotation indicates that only the data set owner or the userare permitted to delete a data set, various identified users may bepermitted to access the data set for reading, and others are altogetherexcluded from accessing the data set. However, other access restrictionparameters may also be used allowing various entities to access a dataset with various permission levels as appropriate.

One skilled in the art will also appreciate that, for security reasons,any databases, systems, devices, servers or other components of thesystem may consist of any combination thereof at a single location or atmultiple locations, wherein each database or system includes any ofvarious suitable security features, such as firewalls, access codes,encryption, decryption, compression, decompression, and/or the like.Encryption may be performed by way of any of the techniques nowavailable in the art or which may become available.

Middleware may include any hardware and/or software suitably configuredto facilitate communications and/or process transactions betweendisparate computing systems. Middleware components are commerciallyavailable and known in the art. Middleware may be implemented throughcommercially available hardware and/or software, through custom hardwareand/or software components, or through a combination thereof. Middlewaremay reside in a variety of configurations and may exist as a standalonesystem or may be a software component residing on the Internet server.

Practitioners will also appreciate that there are a number of methodsfor displaying data within a browser-based document. Data may berepresented as standard text or within a fixed list, scrollable list,drop-down list, editable text field, fixed text field, pop-up window,and the like. Likewise, there are a number of methods available formodifying data in a web page such as, for example, free text entry usinga keyboard, selection of menu items, check boxes, option boxes, and thelike.

In various embodiments, systems may be described herein in terms offunctional block components, screen shots, optional selections andvarious processing steps.

In various embodiments, systems are described herein with reference toscreen shots, block diagrams and flowchart illustrations of methods,apparatus (e.g., systems), and computer program products. Thesefunctional blocks may be realized by any number of hardware and/orsoftware components configured to perform the specified functions. Thesefunctional blocks flowchart illustrations, and combinations offunctional blocks in the block diagrams and flowchart illustrations,respectively, may also be implemented by computer program instructions.

These computer program instructions may be loaded onto a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructionsthat execute on the computer or other programmable data processingapparatus create a computer system capable of implementing the functionsspecified in the flowchart block or blocks. These computer programinstructions may also be stored in a computer-readable memory that candirect a computer or other programmable data processing apparatus tofunction in a particular manner, such that the instructions stored inthe computer-readable memory produce an article of manufacture includinginstruction means which implement the function specified in theflowchart block or blocks. The computer program instructions may also beloaded onto a computer or other programmable data processing apparatusto cause a series of operational steps to be performed on the computeror other programmable apparatus to produce a computer-implementedprocess such that the instructions which execute on the computer orother programmable apparatus provide steps for implementing thefunctions specified in the flowchart block or blocks.

Accordingly, functional blocks of the block diagrams and flowchartillustrations support combinations of hardware, software, and/orhardware-software systems for performing the specified functions,combinations of steps for performing the specified functions, andprogram instruction means for performing the specified functions. Itwill also be understood that each functional block of the block diagramsand flowchart illustrations, and combinations of functional blocks inthe block diagrams and flowchart illustrations, can be implemented byeither special purpose hardware-based computer systems which perform thespecified functions or steps, or suitable combinations of specialpurpose hardware and computer instructions. Further, illustrations ofthe process flows and the descriptions thereof may make reference touser windows, webpages, websites, web forms, prompts, etc. Practitionerswill appreciate that the illustrated steps described herein may comprisein any number of configurations including the use of windows, webpages,web forms, popup windows, prompts and the like. It should be furtherappreciated that the multiple steps as illustrated and described may becombined into single webpages and/or windows but have been expanded forthe sake of simplicity. In other cases, steps illustrated and describedas single process steps may be separated into multiple webpages and/orwindows but have been combined for simplicity.

The term “non-transitory” is to be understood to remove only propagatingtransitory signals per se from the claim scope and does not relinquishrights to all standard computer-readable media that are not onlypropagating transitory signals per se. Stated another way, the meaningof the term “non-transitory computer-readable medium” and“non-transitory computer-readable storage medium” should be construed toexclude only those types of transitory computer-readable media whichwere found in In Re Nuijten to fall outside the scope of patentablesubject matter under 35 U.S.C. §101.

Benefits, other advantages, and solutions to problems have beendescribed herein with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any elements that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as critical, required, or essentialfeatures or elements of the disclosure. The scope of the disclosure isaccordingly to be limited by nothing other than the appended claims, inwhich reference to an element in the singular is not intended to mean“one and only one” unless explicitly so stated, but rather “one ormore.” Moreover, where a phrase similar to ‘at least one of A, B, and C’or ‘at least one of A, B, or C’ is used in the claims or specification,it is intended that the phrase be interpreted to mean that A alone maybe present in an embodiment, B alone may be present in an embodiment, Calone may be present in an embodiment, or that any combination of theelements A, B and C may be present in a single embodiment; for example,A and B, A and C, B and C, or A and B and C. Although the disclosureincludes a method, it is contemplated that it may be embodied ascomputer program instructions on a tangible, non-transitory memory orcomputer-readable carrier, such as a magnetic or optical memory or amagnetic or optical disk. All structural, chemical, and functionalequivalents to the elements of the above-described exemplary embodimentsthat are known to those of ordinary skill in the art are expresslyincorporated herein by reference and are intended to be encompassed bythe present claims. Moreover, it is not necessary for a device or methodto address each and every problem sought to be solved by the presentdisclosure, for it to be encompassed by the present claims. Furthermore,no element, component, or method step in the present disclosure isintended to be dedicated to the public regardless of whether theelement, component, or method step is explicitly recited in the claims.No claim element herein is to be construed under the provisions of 35U.S.C. 112(f), unless the element is expressly recited using the phrase“means for.” As used herein, the terms “comprises”, “comprising”, or anyother variation thereof, are intended to cover a non-exclusiveinclusion, such that a process, method, article, or apparatus thatcomprises a list of elements does not include only those elements butmay include other elements not expressly listed or inherent to suchprocess, method, article, or apparatus.

The disclosure further includes the various aspects, embodiments andteachings set forth in appendices A, B, and C, each of which areincorporated into the disclosure in their entirety for all purposes.

1. A method, comprising: determining, by a computer-based systemconfigured to analyze microorganism information and comprising aprocessor, a tangible, and a non-transitory memory, a first valueassociated with an attribute of a microorganism, based on firstinformation from a microorganism detection system; determining, by thecomputer-based system, a second value associated with the attribute ofthe microorganism, based on second information from the microorganismdetection system determining, by the computer-based system, a growthrate based on the first value and the second value; and comparing, bythe computer-based system, the growth rate to a control growth rate. 2.The method of claim 1, wherein the second value is determined inresponse to an event, wherein the microorganism is subjected to acondition, and wherein the condition is associated with the event. 3.The method of claim 1, wherein the control growth rate is at least oneof a predetermined growth rate and a dynamically determined growth rate.4. The method of claim 1, wherein the event is at least one of apredetermined time, a dynamically determined mass, a number ofindividuated microorganisms, and a number of clones.
 5. The method ofclaim 2, wherein the condition is at least one of a temperature, agrowth medium condition, a carbon source, a nitrogen source, an aminoacid, a nutrient, a salt, a metal ion, a cofactor, a pH, a traceelement, a dissolved gas, an antimicrobial agent, an aerobic condition,and an anaerobic condition.
 6. The method of claim 1, wherein themicroorganism detection system comprises an optical detector configuredto perform at least one of brightfield imaging, darkfield imaging, phasecontrast imaging, fluorescence imaging, upconverting phosphor imaging,chemiluminscence imaging, evanescent imaging, near infra-red detection,confocal microscopy with scattering, and optical densitometry.
 7. Themethod of claim 1, wherein the microorganism detection system isconfigured to perform darkfield imaging and fluorescence imaging.
 8. Themethod of claim 1, wherein the microorganism detection system isconfigured to perform detection using a method selected from a groupconsisting of atomic force microscopy, impedance, electrochemicalimpedance spectroscopy, fluorescence spectroscopy, diffuse reflectancespectroscopy, infrared spectroscopy, terahertz spectroscopy,transmission and absorbance spectroscopy, Raman spectroscopy, includingSurface Enhanced Raman Spectroscopy, Spatially Offset Ramanspectroscopy, transmission Raman spectroscopy, resonance Ramanspectroscopy, MALDI-TOF mass spectrometry, desorption electrosprayionization mass spectrometry, GC mass spectrometry, LC massspectrometry, electrospray ionization mass spectrometry and Selected IonFlow Tube spectrometry, surface plasmon resonance, nephelometry, flowcytometry, capillary electrophoresis, molecular diagnostics, quartzcrystal microbalance, bioluminescence, microcantilever sensors, andasynchronous magnetic bead rotation.
 9. The method of claim 1, furthercomprising: determining, by the computer-based system, a clone signalintensity curve shape likelihood; determining, by the computer-basedsystem, a tracking error likelihood; calculating, by the computer-basedsystem, a growth likelihood value based on the clone signal intensitycurve shape likelihood and tracking error likelihood; determining, bythe computer-based system, at least one of microorganism susceptibilityto an antimicrobial agent, a lack of microorganism susceptibility to anantimicrobial agent, microorganism resistance to an antimicrobial agent,microorganism expression of a virulence factor, microorganismhypervirulence, and a polymicrobial specimen, based on a comparison ofthe growth likelihood value to a reference range.
 10. The method ofclaim 1, further comprising, rendering, by the computer-based system, asignal associated with the microorganism into a plurality of signalapproximations, wherein the plurality of signal approximations areplanes comprising a plurality of point amplitudes corresponding tomicroorganism locations; combining, by the computer-based system, theplurality of signal approximations to create a microorganism model;analyzing, by the computer-based system, the plurality of pointamplitudes associated with at least one of background information andnoise information; filtering by the computer-based system, the pluralityof signal approximations to eliminate at least one of backgroundinformation and noise information; and registering, by thecomputer-based system, locations associated with point amplitudescorresponding to microorganisms.
 11. The method of claim 1, wherein themicroorganism is an individuated microorganism.
 12. A method,comprising: detecting, by a computer-based system configured to analyzemicroorganism information and comprising a processor and a tangible,non-transitory memory, first microorganism information from amicroorganism detection system; detecting, by the computer-based system,second microorganism information from the microorganism detectionsystem; parsing, by the computer-based system, first microorganisminformation and second microorganism information into a plurality ofmicroorganism information value subsets, wherein a first microorganisminformation value subset created from first microorganism informationand second microorganism information value subset created from secondmicroorganism information are associated with a location; associating,by the computer-based system, the first microorganism information valuesubset and the second microorganism information value subset;determining, by the computer-based system, a first growth rate of amicroorganism, based on the first microorganism information value subsetand the second microorganism information value subset, in response tosubjecting a microorganism to at least one of a first event and a firstcondition; obtaining, by the computer-based system, a second valuecorresponding to a reference growth rate; and determining, by thecomputer-based system, a proportional relationship between the firstvalue to second value.
 13. The method of claim 12, further comprisingevaluating, by the computer-based system, the proportional relationshipagainst a reference range.
 14. The method of claim 12, furthercomprising identifying, by the computer-based system, at least one ofmicroorganism susceptibility to an antimicrobial agent, a lack ofmicroorganism susceptibility to an antimicrobial agent, microorganismresistance to an antimicrobial agent, microorganism expression of avirulence factor, microorganism hypervirulence, and polymicrobialspecimens, in response to the proportional relationship falling one ofwithin and outside of the reference range.
 15. An apparatus comprisingmeans for performing the method of any of claim
 1. 16. A computerprogram that, when run on a computer, performs the method of any ofclaim 1.